{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Riskfolio-Lib Tutorial: \n",
    "<br>__[Financionerioncios](https://financioneroncios.wordpress.com)__\n",
    "<br>__[Orenji](https://www.orenj-i.net)__\n",
    "<br>__[Riskfolio-Lib](https://riskfolio-lib.readthedocs.io/en/latest/)__\n",
    "<br>__[Dany Cajas](https://www.linkedin.com/in/dany-cajas/)__\n",
    "<a href='https://ko-fi.com/B0B833SXD' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://cdn.ko-fi.com/cdn/kofi1.png?v=2' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a> \n",
    "\n",
    "## Tutorial 9: Portfolio Optimization with Risk Factors and Principal Components Regression (PCR)\n",
    "\n",
    "## 1. Downloading the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  30 of 30 completed\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import yfinance as yf\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "yf.pdr_override()\n",
    "pd.options.display.float_format = '{:.4%}'.format\n",
    "\n",
    "# Date range\n",
    "start = '2016-01-01'\n",
    "end = '2019-12-30'\n",
    "\n",
    "# Tickers of assets\n",
    "assets = ['JCI', 'TGT', 'CMCSA', 'CPB', 'MO', 'APA', 'MMC', 'JPM',\n",
    "          'ZION', 'PSA', 'BAX', 'BMY', 'LUV', 'PCAR', 'TXT', 'TMO',\n",
    "          'DE', 'MSFT', 'HPQ', 'SEE', 'VZ', 'CNP', 'NI', 'T', 'BA']\n",
    "assets.sort()\n",
    "\n",
    "# Tickers of factors\n",
    "\n",
    "factors = ['MTUM', 'QUAL', 'VLUE', 'SIZE', 'USMV']\n",
    "factors.sort()\n",
    "\n",
    "tickers = assets + factors\n",
    "tickers.sort()\n",
    "\n",
    "# Downloading data\n",
    "data = yf.download(tickers, start = start, end = end)\n",
    "data = data.loc[:,('Adj Close', slice(None))]\n",
    "data.columns = tickers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MTUM</th>\n",
       "      <th>QUAL</th>\n",
       "      <th>SIZE</th>\n",
       "      <th>USMV</th>\n",
       "      <th>VLUE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>0.4735%</td>\n",
       "      <td>0.2672%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.6779%</td>\n",
       "      <td>0.1635%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>-0.5267%</td>\n",
       "      <td>-1.1914%</td>\n",
       "      <td>-0.5380%</td>\n",
       "      <td>-0.6253%</td>\n",
       "      <td>-1.8277%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>-2.2293%</td>\n",
       "      <td>-2.3798%</td>\n",
       "      <td>-1.7181%</td>\n",
       "      <td>-1.6215%</td>\n",
       "      <td>-2.1609%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>-0.9548%</td>\n",
       "      <td>-1.1377%</td>\n",
       "      <td>-1.1978%</td>\n",
       "      <td>-1.0086%</td>\n",
       "      <td>-1.0873%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-11</th>\n",
       "      <td>0.6043%</td>\n",
       "      <td>0.1479%</td>\n",
       "      <td>-0.5898%</td>\n",
       "      <td>0.1491%</td>\n",
       "      <td>-0.6183%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               MTUM     QUAL     SIZE     USMV     VLUE\n",
       "Date                                                   \n",
       "2016-01-05  0.4735%  0.2672%  0.0000%  0.6779%  0.1635%\n",
       "2016-01-06 -0.5267% -1.1914% -0.5380% -0.6253% -1.8277%\n",
       "2016-01-07 -2.2293% -2.3798% -1.7181% -1.6215% -2.1609%\n",
       "2016-01-08 -0.9548% -1.1377% -1.1978% -1.0086% -1.0873%\n",
       "2016-01-11  0.6043%  0.1479% -0.5898%  0.1491% -0.6183%"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculating returns\n",
    "\n",
    "X = data[factors].pct_change().dropna()\n",
    "Y = data[assets].pct_change().dropna()\n",
    "\n",
    "display(X.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Estimating Mean Variance Portfolios with PCR\n",
    "\n",
    "### 2.1 Estimating the loadings matrix with PCR.\n",
    "\n",
    "This part is just to visualize how Riskfolio-Lib calculates a loadings matrix using PCR."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_7a4d3_row0_col0,#T_7a4d3_row0_col1,#T_7a4d3_row14_col3,#T_7a4d3_row15_col2,#T_7a4d3_row15_col5,#T_7a4d3_row24_col4{\n",
       "            background-color:  #a50026;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_7a4d3_row0_col2{\n",
       "            background-color:  #0d8044;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_7a4d3_row0_col3,#T_7a4d3_row0_col5,#T_7a4d3_row10_col0,#T_7a4d3_row14_col1,#T_7a4d3_row14_col2,#T_7a4d3_row15_col4{\n",
       "            background-color:  #006837;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_7a4d3_row0_col4{\n",
       "            background-color:  #af0926;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_7a4d3_row1_col0,#T_7a4d3_row14_col0{\n",
       "            background-color:  #0f8446;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_7a4d3_row1_col1{\n",
       "            background-color:  #eef8a8;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row1_col2,#T_7a4d3_row2_col0{\n",
       "            background-color:  #5ab760;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row1_col3{\n",
       "            background-color:  #feec9f;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row1_col4{\n",
       "            background-color:  #fee491;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row1_col5,#T_7a4d3_row4_col2,#T_7a4d3_row17_col3,#T_7a4d3_row19_col3{\n",
       "            background-color:  #e3f399;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row2_col1,#T_7a4d3_row22_col3{\n",
       "            background-color:  #d9ef8b;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row2_col2{\n",
       "            background-color:  #daf08d;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row2_col3{\n",
       "            background-color:  #feca79;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row2_col4{\n",
       "            background-color:  #cfeb85;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row2_col5{\n",
       "            background-color:  #fec877;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row3_col0{\n",
       "            background-color:  #f88950;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row3_col1,#T_7a4d3_row7_col1,#T_7a4d3_row11_col0{\n",
       "            background-color:  #feefa3;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row3_col2,#T_7a4d3_row18_col4{\n",
       "            background-color:  #f1f9ac;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row3_col3{\n",
       "            background-color:  #fff8b4;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row3_col4{\n",
       "            background-color:  #eff8aa;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row3_col5,#T_7a4d3_row12_col1{\n",
       "            background-color:  #fee593;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row4_col0,#T_7a4d3_row5_col0,#T_7a4d3_row20_col2{\n",
       "            background-color:  #cbe982;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row4_col1{\n",
       "            background-color:  #fff0a6;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row4_col3,#T_7a4d3_row14_col4{\n",
       "            background-color:  #fffbb8;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row4_col4,#T_7a4d3_row20_col5{\n",
       "            background-color:  #f2faae;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row4_col5,#T_7a4d3_row10_col1{\n",
       "            background-color:  #feeb9d;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row5_col1,#T_7a4d3_row13_col2{\n",
       "            background-color:  #f99153;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row5_col2{\n",
       "            background-color:  #e75337;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row5_col3,#T_7a4d3_row18_col3{\n",
       "            background-color:  #b5df74;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row5_col4{\n",
       "            background-color:  #4bb05c;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row5_col5{\n",
       "            background-color:  #e95538;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row6_col0{\n",
       "            background-color:  #fba05b;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row6_col1{\n",
       "            background-color:  #f99355;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row6_col2{\n",
       "            background-color:  #d62f27;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_7a4d3_row6_col3,#T_7a4d3_row16_col3{\n",
       "            background-color:  #c1e57b;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row6_col4{\n",
       "            background-color:  #39a758;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row6_col5{\n",
       "            background-color:  #dd3d2d;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_7a4d3_row7_col0{\n",
       "            background-color:  #097940;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_7a4d3_row7_col2{\n",
       "            background-color:  #3ca959;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row7_col3,#T_7a4d3_row15_col0{\n",
       "            background-color:  #f7fcb4;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row7_col4,#T_7a4d3_row23_col1{\n",
       "            background-color:  #fdad60;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row7_col5{\n",
       "            background-color:  #b1de71;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row8_col0{\n",
       "            background-color:  #9dd569;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row8_col1,#T_7a4d3_row18_col2{\n",
       "            background-color:  #f8fcb6;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row8_col2{\n",
       "            background-color:  #07753e;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_7a4d3_row8_col3{\n",
       "            background-color:  #fffab6;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row8_col4{\n",
       "            background-color:  #fa9656;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row8_col5{\n",
       "            background-color:  #96d268;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row9_col0{\n",
       "            background-color:  #e6f59d;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row9_col1,#T_7a4d3_row11_col4{\n",
       "            background-color:  #fece7c;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row9_col2{\n",
       "            background-color:  #c3e67d;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row9_col3{\n",
       "            background-color:  #e5f49b;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row9_col4{\n",
       "            background-color:  #fee28f;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row9_col5{\n",
       "            background-color:  #e9f6a1;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row10_col2,#T_7a4d3_row17_col4{\n",
       "            background-color:  #15904c;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row10_col3,#T_7a4d3_row12_col2{\n",
       "            background-color:  #fffcba;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row10_col4{\n",
       "            background-color:  #e54e35;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row10_col5{\n",
       "            background-color:  #6ec064;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row11_col1,#T_7a4d3_row12_col5{\n",
       "            background-color:  #fed07e;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row11_col2{\n",
       "            background-color:  #9bd469;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row11_col3{\n",
       "            background-color:  #dff293;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row11_col5,#T_7a4d3_row21_col1{\n",
       "            background-color:  #d5ed88;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row12_col0{\n",
       "            background-color:  #66bd63;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row12_col3,#T_7a4d3_row19_col0{\n",
       "            background-color:  #f4fab0;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row12_col4{\n",
       "            background-color:  #d3ec87;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row13_col0{\n",
       "            background-color:  #f57547;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row13_col1,#T_7a4d3_row17_col1,#T_7a4d3_row19_col1{\n",
       "            background-color:  #fdbb6c;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row13_col3{\n",
       "            background-color:  #ddf191;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row13_col4,#T_7a4d3_row22_col5{\n",
       "            background-color:  #87cb67;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row13_col5{\n",
       "            background-color:  #f67f4b;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row14_col5{\n",
       "            background-color:  #fff6b0;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row15_col1{\n",
       "            background-color:  #fba35c;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row15_col3{\n",
       "            background-color:  #c5e67e;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row16_col0{\n",
       "            background-color:  #7fc866;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row16_col1{\n",
       "            background-color:  #fdb96a;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row16_col2{\n",
       "            background-color:  #45ad5b;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row16_col4,#T_7a4d3_row22_col4,#T_7a4d3_row23_col2{\n",
       "            background-color:  #f7844e;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row16_col5{\n",
       "            background-color:  #89cc67;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row17_col0{\n",
       "            background-color:  #e44c34;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row17_col2{\n",
       "            background-color:  #c82227;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_7a4d3_row17_col5{\n",
       "            background-color:  #c21c27;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_7a4d3_row18_col0{\n",
       "            background-color:  #db382b;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_7a4d3_row18_col1{\n",
       "            background-color:  #fca85e;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row18_col5{\n",
       "            background-color:  #fff2aa;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row19_col2,#T_7a4d3_row21_col5{\n",
       "            background-color:  #fee695;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row19_col4{\n",
       "            background-color:  #ecf7a6;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row19_col5{\n",
       "            background-color:  #fee08b;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row20_col0{\n",
       "            background-color:  #128a49;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row20_col1{\n",
       "            background-color:  #feea9b;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row20_col3{\n",
       "            background-color:  #fff5ae;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row20_col4,#T_7a4d3_row21_col3{\n",
       "            background-color:  #fede89;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row21_col0{\n",
       "            background-color:  #8ccd67;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row21_col2{\n",
       "            background-color:  #a0d669;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row21_col4{\n",
       "            background-color:  #e0f295;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row22_col0{\n",
       "            background-color:  #f36b42;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row22_col1{\n",
       "            background-color:  #fed27f;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row22_col2{\n",
       "            background-color:  #249d53;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row23_col0,#T_7a4d3_row23_col3{\n",
       "            background-color:  #d7ee8a;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row23_col4{\n",
       "            background-color:  #8ecf67;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row23_col5{\n",
       "            background-color:  #f7814c;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row24_col0{\n",
       "            background-color:  #0b7d42;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_7a4d3_row24_col1{\n",
       "            background-color:  #fa9857;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row24_col2{\n",
       "            background-color:  #04703b;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_7a4d3_row24_col3{\n",
       "            background-color:  #bbe278;\n",
       "            color:  #000000;\n",
       "        }#T_7a4d3_row24_col5{\n",
       "            background-color:  #0e8245;\n",
       "            color:  #f1f1f1;\n",
       "        }</style><table id=\"T_7a4d3_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >const</th>        <th class=\"col_heading level0 col1\" >MTUM</th>        <th class=\"col_heading level0 col2\" >QUAL</th>        <th class=\"col_heading level0 col3\" >SIZE</th>        <th class=\"col_heading level0 col4\" >USMV</th>        <th class=\"col_heading level0 col5\" >VLUE</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_7a4d3_level0_row0\" class=\"row_heading level0 row0\" >APA</th>\n",
       "                        <td id=\"T_7a4d3_row0_col0\" class=\"data row0 col0\" >-0.0006</td>\n",
       "                        <td id=\"T_7a4d3_row0_col1\" class=\"data row0 col1\" >-0.6174</td>\n",
       "                        <td id=\"T_7a4d3_row0_col2\" class=\"data row0 col2\" >0.3847</td>\n",
       "                        <td id=\"T_7a4d3_row0_col3\" class=\"data row0 col3\" >1.0857</td>\n",
       "                        <td id=\"T_7a4d3_row0_col4\" class=\"data row0 col4\" >-1.1434</td>\n",
       "                        <td id=\"T_7a4d3_row0_col5\" class=\"data row0 col5\" >1.4537</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7a4d3_level0_row1\" class=\"row_heading level0 row1\" >BA</th>\n",
       "                        <td id=\"T_7a4d3_row1_col0\" class=\"data row1 col0\" >0.0005</td>\n",
       "                        <td id=\"T_7a4d3_row1_col1\" class=\"data row1 col1\" >0.2236</td>\n",
       "                        <td id=\"T_7a4d3_row1_col2\" class=\"data row1 col2\" >0.3103</td>\n",
       "                        <td id=\"T_7a4d3_row1_col3\" class=\"data row1 col3\" >0.2055</td>\n",
       "                        <td id=\"T_7a4d3_row1_col4\" class=\"data row1 col4\" >-0.0523</td>\n",
       "                        <td id=\"T_7a4d3_row1_col5\" class=\"data row1 col5\" >0.4583</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7a4d3_level0_row2\" class=\"row_heading level0 row2\" >BAX</th>\n",
       "                        <td id=\"T_7a4d3_row2_col0\" class=\"data row2 col0\" >0.0003</td>\n",
       "                        <td id=\"T_7a4d3_row2_col1\" class=\"data row2 col1\" >0.3086</td>\n",
       "                        <td id=\"T_7a4d3_row2_col2\" class=\"data row2 col2\" >0.1875</td>\n",
       "                        <td id=\"T_7a4d3_row2_col3\" class=\"data row2 col3\" >0.0764</td>\n",
       "                        <td id=\"T_7a4d3_row2_col4\" class=\"data row2 col4\" >0.5223</td>\n",
       "                        <td id=\"T_7a4d3_row2_col5\" class=\"data row2 col5\" >-0.0462</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7a4d3_level0_row3\" class=\"row_heading level0 row3\" >BMY</th>\n",
       "                        <td id=\"T_7a4d3_row3_col0\" class=\"data row3 col0\" >-0.0003</td>\n",
       "                        <td id=\"T_7a4d3_row3_col1\" class=\"data row3 col1\" >0.0716</td>\n",
       "                        <td id=\"T_7a4d3_row3_col2\" class=\"data row3 col2\" >0.1550</td>\n",
       "                        <td id=\"T_7a4d3_row3_col3\" class=\"data row3 col3\" >0.2640</td>\n",
       "                        <td id=\"T_7a4d3_row3_col4\" class=\"data row3 col4\" >0.3029</td>\n",
       "                        <td id=\"T_7a4d3_row3_col5\" class=\"data row3 col5\" >0.1028</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7a4d3_level0_row4\" class=\"row_heading level0 row4\" >CMCSA</th>\n",
       "                        <td id=\"T_7a4d3_row4_col0\" class=\"data row4 col0\" >0.0001</td>\n",
       "                        <td id=\"T_7a4d3_row4_col1\" class=\"data row4 col1\" >0.0825</td>\n",
       "                        <td id=\"T_7a4d3_row4_col2\" class=\"data row4 col2\" >0.1750</td>\n",
       "                        <td id=\"T_7a4d3_row4_col3\" class=\"data row4 col3\" >0.2755</td>\n",
       "                        <td id=\"T_7a4d3_row4_col4\" class=\"data row4 col4\" >0.2803</td>\n",
       "                        <td id=\"T_7a4d3_row4_col5\" class=\"data row4 col5\" >0.1424</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7a4d3_level0_row5\" class=\"row_heading level0 row5\" >CNP</th>\n",
       "                        <td id=\"T_7a4d3_row5_col0\" class=\"data row5 col0\" >0.0001</td>\n",
       "                        <td id=\"T_7a4d3_row5_col1\" class=\"data row5 col1\" >-0.2225</td>\n",
       "                        <td id=\"T_7a4d3_row5_col2\" class=\"data row5 col2\" >-0.0563</td>\n",
       "                        <td id=\"T_7a4d3_row5_col3\" class=\"data row5 col3\" >0.5690</td>\n",
       "                        <td id=\"T_7a4d3_row5_col4\" class=\"data row5 col4\" >1.1192</td>\n",
       "                        <td id=\"T_7a4d3_row5_col5\" class=\"data row5 col5\" >-0.4869</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7a4d3_level0_row6\" class=\"row_heading level0 row6\" >CPB</th>\n",
       "                        <td id=\"T_7a4d3_row6_col0\" class=\"data row6 col0\" >-0.0003</td>\n",
       "                        <td id=\"T_7a4d3_row6_col1\" class=\"data row6 col1\" >-0.2158</td>\n",
       "                        <td id=\"T_7a4d3_row6_col2\" class=\"data row6 col2\" >-0.0890</td>\n",
       "                        <td id=\"T_7a4d3_row6_col3\" class=\"data row6 col3\" >0.5280</td>\n",
       "                        <td id=\"T_7a4d3_row6_col4\" class=\"data row6 col4\" >1.1880</td>\n",
       "                        <td id=\"T_7a4d3_row6_col5\" class=\"data row6 col5\" >-0.5770</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7a4d3_level0_row7\" class=\"row_heading level0 row7\" >DE</th>\n",
       "                        <td id=\"T_7a4d3_row7_col0\" class=\"data row7 col0\" >0.0005</td>\n",
       "                        <td id=\"T_7a4d3_row7_col1\" class=\"data row7 col1\" >0.0756</td>\n",
       "                        <td id=\"T_7a4d3_row7_col2\" class=\"data row7 col2\" >0.3323</td>\n",
       "                        <td id=\"T_7a4d3_row7_col3\" class=\"data row7 col3\" >0.3341</td>\n",
       "                        <td id=\"T_7a4d3_row7_col4\" class=\"data row7 col4\" >-0.3697</td>\n",
       "                        <td id=\"T_7a4d3_row7_col5\" class=\"data row7 col5\" >0.7041</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7a4d3_level0_row8\" class=\"row_heading level0 row8\" >HPQ</th>\n",
       "                        <td id=\"T_7a4d3_row8_col0\" class=\"data row8 col0\" >0.0002</td>\n",
       "                        <td id=\"T_7a4d3_row8_col1\" class=\"data row8 col1\" >0.1799</td>\n",
       "                        <td id=\"T_7a4d3_row8_col2\" class=\"data row8 col2\" >0.3985</td>\n",
       "                        <td id=\"T_7a4d3_row8_col3\" class=\"data row8 col3\" >0.2753</td>\n",
       "                        <td id=\"T_7a4d3_row8_col4\" class=\"data row8 col4\" >-0.4646</td>\n",
       "                        <td id=\"T_7a4d3_row8_col5\" class=\"data row8 col5\" >0.8125</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7a4d3_level0_row9\" class=\"row_heading level0 row9\" >JCI</th>\n",
       "                        <td id=\"T_7a4d3_row9_col0\" class=\"data row9 col0\" >0.0000</td>\n",
       "                        <td id=\"T_7a4d3_row9_col1\" class=\"data row9 col1\" >-0.0535</td>\n",
       "                        <td id=\"T_7a4d3_row9_col2\" class=\"data row9 col2\" >0.2147</td>\n",
       "                        <td id=\"T_7a4d3_row9_col3\" class=\"data row9 col3\" >0.4089</td>\n",
       "                        <td id=\"T_7a4d3_row9_col4\" class=\"data row9 col4\" >-0.0640</td>\n",
       "                        <td id=\"T_7a4d3_row9_col5\" class=\"data row9 col5\" >0.4251</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7a4d3_level0_row10\" class=\"row_heading level0 row10\" >JPM</th>\n",
       "                        <td id=\"T_7a4d3_row10_col0\" class=\"data row10 col0\" >0.0005</td>\n",
       "                        <td id=\"T_7a4d3_row10_col1\" class=\"data row10 col1\" >0.0537</td>\n",
       "                        <td id=\"T_7a4d3_row10_col2\" class=\"data row10 col2\" >0.3676</td>\n",
       "                        <td id=\"T_7a4d3_row10_col3\" class=\"data row10 col3\" >0.2851</td>\n",
       "                        <td id=\"T_7a4d3_row10_col4\" class=\"data row10 col4\" >-0.7861</td>\n",
       "                        <td id=\"T_7a4d3_row10_col5\" class=\"data row10 col5\" >0.9582</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7a4d3_level0_row11\" class=\"row_heading level0 row11\" >LUV</th>\n",
       "                        <td id=\"T_7a4d3_row11_col0\" class=\"data row11 col0\" >-0.0001</td>\n",
       "                        <td id=\"T_7a4d3_row11_col1\" class=\"data row11 col1\" >-0.0477</td>\n",
       "                        <td id=\"T_7a4d3_row11_col2\" class=\"data row11 col2\" >0.2557</td>\n",
       "                        <td id=\"T_7a4d3_row11_col3\" class=\"data row11 col3\" >0.4299</td>\n",
       "                        <td id=\"T_7a4d3_row11_col4\" class=\"data row11 col4\" >-0.1855</td>\n",
       "                        <td id=\"T_7a4d3_row11_col5\" class=\"data row11 col5\" >0.5469</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7a4d3_level0_row12\" class=\"row_heading level0 row12\" >MMC</th>\n",
       "                        <td id=\"T_7a4d3_row12_col0\" class=\"data row12 col0\" >0.0003</td>\n",
       "                        <td id=\"T_7a4d3_row12_col1\" class=\"data row12 col1\" >0.0234</td>\n",
       "                        <td id=\"T_7a4d3_row12_col2\" class=\"data row12 col2\" >0.1293</td>\n",
       "                        <td id=\"T_7a4d3_row12_col3\" class=\"data row12 col3\" >0.3482</td>\n",
       "                        <td id=\"T_7a4d3_row12_col4\" class=\"data row12 col4\" >0.4954</td>\n",
       "                        <td id=\"T_7a4d3_row12_col5\" class=\"data row12 col5\" >-0.0041</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7a4d3_level0_row13\" class=\"row_heading level0 row13\" >MO</th>\n",
       "                        <td id=\"T_7a4d3_row13_col0\" class=\"data row13 col0\" >-0.0003</td>\n",
       "                        <td id=\"T_7a4d3_row13_col1\" class=\"data row13 col1\" >-0.1141</td>\n",
       "                        <td id=\"T_7a4d3_row13_col2\" class=\"data row13 col2\" >-0.0021</td>\n",
       "                        <td id=\"T_7a4d3_row13_col3\" class=\"data row13 col3\" >0.4376</td>\n",
       "                        <td id=\"T_7a4d3_row13_col4\" class=\"data row13 col4\" >0.8811</td>\n",
       "                        <td id=\"T_7a4d3_row13_col5\" class=\"data row13 col5\" >-0.3382</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7a4d3_level0_row14\" class=\"row_heading level0 row14\" >MSFT</th>\n",
       "                        <td id=\"T_7a4d3_row14_col0\" class=\"data row14 col0\" >0.0004</td>\n",
       "                        <td id=\"T_7a4d3_row14_col1\" class=\"data row14 col1\" >0.9280</td>\n",
       "                        <td id=\"T_7a4d3_row14_col2\" class=\"data row14 col2\" >0.4153</td>\n",
       "                        <td id=\"T_7a4d3_row14_col3\" class=\"data row14 col3\" >-0.4857</td>\n",
       "                        <td id=\"T_7a4d3_row14_col4\" class=\"data row14 col4\" >0.1509</td>\n",
       "                        <td id=\"T_7a4d3_row14_col5\" class=\"data row14 col5\" >0.2270</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7a4d3_level0_row15\" class=\"row_heading level0 row15\" >NI</th>\n",
       "                        <td id=\"T_7a4d3_row15_col0\" class=\"data row15 col0\" >0.0000</td>\n",
       "                        <td id=\"T_7a4d3_row15_col1\" class=\"data row15 col1\" >-0.1805</td>\n",
       "                        <td id=\"T_7a4d3_row15_col2\" class=\"data row15 col2\" >-0.1459</td>\n",
       "                        <td id=\"T_7a4d3_row15_col3\" class=\"data row15 col3\" >0.5152</td>\n",
       "                        <td id=\"T_7a4d3_row15_col4\" class=\"data row15 col4\" >1.5802</td>\n",
       "                        <td id=\"T_7a4d3_row15_col5\" class=\"data row15 col5\" >-0.8640</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7a4d3_level0_row16\" class=\"row_heading level0 row16\" >PCAR</th>\n",
       "                        <td id=\"T_7a4d3_row16_col0\" class=\"data row16 col0\" >0.0003</td>\n",
       "                        <td id=\"T_7a4d3_row16_col1\" class=\"data row16 col1\" >-0.1179</td>\n",
       "                        <td id=\"T_7a4d3_row16_col2\" class=\"data row16 col2\" >0.3275</td>\n",
       "                        <td id=\"T_7a4d3_row16_col3\" class=\"data row16 col3\" >0.5273</td>\n",
       "                        <td id=\"T_7a4d3_row16_col4\" class=\"data row16 col4\" >-0.5373</td>\n",
       "                        <td id=\"T_7a4d3_row16_col5\" class=\"data row16 col5\" >0.8583</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7a4d3_level0_row17\" class=\"row_heading level0 row17\" >PSA</th>\n",
       "                        <td id=\"T_7a4d3_row17_col0\" class=\"data row17 col0\" >-0.0004</td>\n",
       "                        <td id=\"T_7a4d3_row17_col1\" class=\"data row17 col1\" >-0.1124</td>\n",
       "                        <td id=\"T_7a4d3_row17_col2\" class=\"data row17 col2\" >-0.1054</td>\n",
       "                        <td id=\"T_7a4d3_row17_col3\" class=\"data row17 col3\" >0.4160</td>\n",
       "                        <td id=\"T_7a4d3_row17_col4\" class=\"data row17 col4\" >1.3473</td>\n",
       "                        <td id=\"T_7a4d3_row17_col5\" class=\"data row17 col5\" >-0.7230</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7a4d3_level0_row18\" class=\"row_heading level0 row18\" >SEE</th>\n",
       "                        <td id=\"T_7a4d3_row18_col0\" class=\"data row18 col0\" >-0.0004</td>\n",
       "                        <td id=\"T_7a4d3_row18_col1\" class=\"data row18 col1\" >-0.1698</td>\n",
       "                        <td id=\"T_7a4d3_row18_col2\" class=\"data row18 col2\" >0.1456</td>\n",
       "                        <td id=\"T_7a4d3_row18_col3\" class=\"data row18 col3\" >0.5654</td>\n",
       "                        <td id=\"T_7a4d3_row18_col4\" class=\"data row18 col4\" >0.2944</td>\n",
       "                        <td id=\"T_7a4d3_row18_col5\" class=\"data row18 col5\" >0.2013</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7a4d3_level0_row19\" class=\"row_heading level0 row19\" >T</th>\n",
       "                        <td id=\"T_7a4d3_row19_col0\" class=\"data row19 col0\" >0.0000</td>\n",
       "                        <td id=\"T_7a4d3_row19_col1\" class=\"data row19 col1\" >-0.1160</td>\n",
       "                        <td id=\"T_7a4d3_row19_col2\" class=\"data row19 col2\" >0.0889</td>\n",
       "                        <td id=\"T_7a4d3_row19_col3\" class=\"data row19 col3\" >0.4110</td>\n",
       "                        <td id=\"T_7a4d3_row19_col4\" class=\"data row19 col4\" >0.3300</td>\n",
       "                        <td id=\"T_7a4d3_row19_col5\" class=\"data row19 col5\" >0.0626</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7a4d3_level0_row20\" class=\"row_heading level0 row20\" >TGT</th>\n",
       "                        <td id=\"T_7a4d3_row20_col0\" class=\"data row20 col0\" >0.0004</td>\n",
       "                        <td id=\"T_7a4d3_row20_col1\" class=\"data row20 col1\" >0.0523</td>\n",
       "                        <td id=\"T_7a4d3_row20_col2\" class=\"data row20 col2\" >0.2066</td>\n",
       "                        <td id=\"T_7a4d3_row20_col3\" class=\"data row20 col3\" >0.2454</td>\n",
       "                        <td id=\"T_7a4d3_row20_col4\" class=\"data row20 col4\" >-0.0941</td>\n",
       "                        <td id=\"T_7a4d3_row20_col5\" class=\"data row20 col5\" >0.3757</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7a4d3_level0_row21\" class=\"row_heading level0 row21\" >TMO</th>\n",
       "                        <td id=\"T_7a4d3_row21_col0\" class=\"data row21 col0\" >0.0002</td>\n",
       "                        <td id=\"T_7a4d3_row21_col1\" class=\"data row21 col1\" >0.3213</td>\n",
       "                        <td id=\"T_7a4d3_row21_col2\" class=\"data row21 col2\" >0.2528</td>\n",
       "                        <td id=\"T_7a4d3_row21_col3\" class=\"data row21 col3\" >0.1344</td>\n",
       "                        <td id=\"T_7a4d3_row21_col4\" class=\"data row21 col4\" >0.4176</td>\n",
       "                        <td id=\"T_7a4d3_row21_col5\" class=\"data row21 col5\" >0.1072</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7a4d3_level0_row22\" class=\"row_heading level0 row22\" >TXT</th>\n",
       "                        <td id=\"T_7a4d3_row22_col0\" class=\"data row22 col0\" >-0.0004</td>\n",
       "                        <td id=\"T_7a4d3_row22_col1\" class=\"data row22 col1\" >-0.0401</td>\n",
       "                        <td id=\"T_7a4d3_row22_col2\" class=\"data row22 col2\" >0.3498</td>\n",
       "                        <td id=\"T_7a4d3_row22_col3\" class=\"data row22 col3\" >0.4581</td>\n",
       "                        <td id=\"T_7a4d3_row22_col4\" class=\"data row22 col4\" >-0.5477</td>\n",
       "                        <td id=\"T_7a4d3_row22_col5\" class=\"data row22 col5\" >0.8658</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7a4d3_level0_row23\" class=\"row_heading level0 row23\" >VZ</th>\n",
       "                        <td id=\"T_7a4d3_row23_col0\" class=\"data row23 col0\" >0.0001</td>\n",
       "                        <td id=\"T_7a4d3_row23_col1\" class=\"data row23 col1\" >-0.1570</td>\n",
       "                        <td id=\"T_7a4d3_row23_col2\" class=\"data row23 col2\" >-0.0138</td>\n",
       "                        <td id=\"T_7a4d3_row23_col3\" class=\"data row23 col3\" >0.4624</td>\n",
       "                        <td id=\"T_7a4d3_row23_col4\" class=\"data row23 col4\" >0.8432</td>\n",
       "                        <td id=\"T_7a4d3_row23_col5\" class=\"data row23 col5\" >-0.3221</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7a4d3_level0_row24\" class=\"row_heading level0 row24\" >ZION</th>\n",
       "                        <td id=\"T_7a4d3_row24_col0\" class=\"data row24 col0\" >0.0005</td>\n",
       "                        <td id=\"T_7a4d3_row24_col1\" class=\"data row24 col1\" >-0.2022</td>\n",
       "                        <td id=\"T_7a4d3_row24_col2\" class=\"data row24 col2\" >0.4050</td>\n",
       "                        <td id=\"T_7a4d3_row24_col3\" class=\"data row24 col3\" >0.5509</td>\n",
       "                        <td id=\"T_7a4d3_row24_col4\" class=\"data row24 col4\" >-1.1994</td>\n",
       "                        <td id=\"T_7a4d3_row24_col5\" class=\"data row24 col5\" >1.3225</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fdcda2248b0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import riskfolio.ParamsEstimation as pe\n",
    "\n",
    "feature_selection = 'PCR' # Method to select best model, could be PCR or Stepwise\n",
    "n_components = 0.95 # 95% of explained variance. See PCA in scikit learn for more information\n",
    "\n",
    "loadings = pe.loadings_matrix(X=X, Y=Y, feature_selection=feature_selection,\n",
    "                              n_components=n_components)\n",
    "\n",
    "loadings.style.format(\"{:.4f}\").background_gradient(cmap='RdYlGn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Calculating the portfolio that maximizes Sharpe ratio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>APA</th>\n",
       "      <th>BA</th>\n",
       "      <th>BAX</th>\n",
       "      <th>BMY</th>\n",
       "      <th>CMCSA</th>\n",
       "      <th>CNP</th>\n",
       "      <th>CPB</th>\n",
       "      <th>DE</th>\n",
       "      <th>HPQ</th>\n",
       "      <th>JCI</th>\n",
       "      <th>...</th>\n",
       "      <th>NI</th>\n",
       "      <th>PCAR</th>\n",
       "      <th>PSA</th>\n",
       "      <th>SEE</th>\n",
       "      <th>T</th>\n",
       "      <th>TGT</th>\n",
       "      <th>TMO</th>\n",
       "      <th>TXT</th>\n",
       "      <th>VZ</th>\n",
       "      <th>ZION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>weights</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>5.2498%</td>\n",
       "      <td>11.0551%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>10.4527%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>4.5016%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>...</td>\n",
       "      <td>10.9344%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>5.0770%</td>\n",
       "      <td>0.8006%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>5.7562%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            APA      BA      BAX     BMY   CMCSA      CNP     CPB      DE  \\\n",
       "weights 0.0000% 5.2498% 11.0551% 0.0000% 0.0000% 10.4527% 0.0000% 4.5016%   \n",
       "\n",
       "            HPQ     JCI  ...       NI    PCAR     PSA     SEE       T     TGT  \\\n",
       "weights 0.0000% 0.0000%  ... 10.9344% 0.0000% 0.0000% 0.0000% 0.0000% 5.0770%   \n",
       "\n",
       "            TMO     TXT      VZ    ZION  \n",
       "weights 0.8006% 0.0000% 5.7562% 0.0000%  \n",
       "\n",
       "[1 rows x 25 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import riskfolio.Portfolio as pf\n",
    "\n",
    "# Building the portfolio object\n",
    "port = pf.Portfolio(returns=Y)\n",
    "\n",
    "# Calculating optimum portfolio\n",
    "\n",
    "# Select method and estimate input parameters:\n",
    "\n",
    "method_mu='hist' # Method to estimate expected returns based on historical data.\n",
    "method_cov='hist' # Method to estimate covariance matrix based on historical data.\n",
    "\n",
    "port.assets_stats(method_mu=method_mu,\n",
    "                  method_cov=method_cov)\n",
    "\n",
    "feature_selection = 'PCR' # Method to select best model, could be PCR or Stepwise\n",
    "n_components = 0.95 # 95% of explained variance. See PCA in scikit learn for more information\n",
    "\n",
    "port.factors = X\n",
    "port.factors_stats(method_mu=method_mu,\n",
    "                   method_cov=method_cov,\n",
    "                   feature_selection=feature_selection,\n",
    "                   n_components=n_components\n",
    "                  )\n",
    "\n",
    "# Estimate optimal portfolio:\n",
    "\n",
    "model='FM' # Factor Model\n",
    "rm = 'MV' # Risk measure used, this time will be variance\n",
    "obj = 'Sharpe' # Objective function, could be MinRisk, MaxRet, Utility or Sharpe\n",
    "hist = False # Use historical scenarios for risk measures that depend on scenarios\n",
    "rf = 0 # Risk free rate\n",
    "l = 0 # Risk aversion factor, only useful when obj is 'Utility'\n",
    "\n",
    "w = port.optimization(model=model, rm=rm, obj=obj, rf=rf, l=l, hist=hist)\n",
    "\n",
    "display(w.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Plotting portfolio composition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import riskfolio.PlotFunctions as plf\n",
    "\n",
    "# Plotting the composition of the portfolio\n",
    "\n",
    "ax = plf.plot_pie(w=w, title='Sharpe FM Mean Variance', others=0.05, nrow=25, cmap = \"tab20\",\n",
    "                  height=6, width=10, ax=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Calculate efficient frontier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>APA</th>\n",
       "      <th>BA</th>\n",
       "      <th>BAX</th>\n",
       "      <th>BMY</th>\n",
       "      <th>CMCSA</th>\n",
       "      <th>CNP</th>\n",
       "      <th>CPB</th>\n",
       "      <th>DE</th>\n",
       "      <th>HPQ</th>\n",
       "      <th>JCI</th>\n",
       "      <th>...</th>\n",
       "      <th>NI</th>\n",
       "      <th>PCAR</th>\n",
       "      <th>PSA</th>\n",
       "      <th>SEE</th>\n",
       "      <th>T</th>\n",
       "      <th>TGT</th>\n",
       "      <th>TMO</th>\n",
       "      <th>TXT</th>\n",
       "      <th>VZ</th>\n",
       "      <th>ZION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.7691%</td>\n",
       "      <td>2.8343%</td>\n",
       "      <td>3.1025%</td>\n",
       "      <td>9.6915%</td>\n",
       "      <td>5.7307%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>2.9399%</td>\n",
       "      <td>...</td>\n",
       "      <td>12.5915%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>12.4035%</td>\n",
       "      <td>1.1702%</td>\n",
       "      <td>10.5504%</td>\n",
       "      <td>3.2020%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>12.5853%</td>\n",
       "      <td>2.2369%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.4079%</td>\n",
       "      <td>5.2900%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>2.9674%</td>\n",
       "      <td>11.6229%</td>\n",
       "      <td>3.8398%</td>\n",
       "      <td>0.9540%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>1.2312%</td>\n",
       "      <td>...</td>\n",
       "      <td>14.2317%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>6.9798%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>8.0211%</td>\n",
       "      <td>4.2045%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>12.7504%</td>\n",
       "      <td>2.2460%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>1.3855%</td>\n",
       "      <td>6.8651%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>2.6475%</td>\n",
       "      <td>12.2969%</td>\n",
       "      <td>2.9565%</td>\n",
       "      <td>1.7118%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.2436%</td>\n",
       "      <td>...</td>\n",
       "      <td>14.8324%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>4.4942%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>6.6988%</td>\n",
       "      <td>4.5286%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>12.6424%</td>\n",
       "      <td>1.9247%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>1.9071%</td>\n",
       "      <td>7.6603%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>2.2215%</td>\n",
       "      <td>12.6950%</td>\n",
       "      <td>2.2652%</td>\n",
       "      <td>2.1053%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>...</td>\n",
       "      <td>15.1614%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>2.5375%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>5.6100%</td>\n",
       "      <td>4.7010%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>12.4571%</td>\n",
       "      <td>1.5950%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>2.3836%</td>\n",
       "      <td>8.3567%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>1.7461%</td>\n",
       "      <td>12.9568%</td>\n",
       "      <td>1.5353%</td>\n",
       "      <td>2.4558%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>...</td>\n",
       "      <td>15.3321%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.5110%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>4.4575%</td>\n",
       "      <td>4.8411%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>12.1574%</td>\n",
       "      <td>1.2390%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      APA      BA     BAX     BMY   CMCSA      CNP     CPB      DE     HPQ  \\\n",
       "0 0.0000% 0.0000% 0.7691% 2.8343% 3.1025%  9.6915% 5.7307% 0.0000% 0.0000%   \n",
       "1 0.0000% 0.4079% 5.2900% 0.0000% 2.9674% 11.6229% 3.8398% 0.9540% 0.0000%   \n",
       "2 0.0000% 1.3855% 6.8651% 0.0000% 2.6475% 12.2969% 2.9565% 1.7118% 0.0000%   \n",
       "3 0.0000% 1.9071% 7.6603% 0.0000% 2.2215% 12.6950% 2.2652% 2.1053% 0.0000%   \n",
       "4 0.0000% 2.3836% 8.3567% 0.0000% 1.7461% 12.9568% 1.5353% 2.4558% 0.0000%   \n",
       "\n",
       "      JCI  ...       NI    PCAR      PSA     SEE        T     TGT     TMO  \\\n",
       "0 2.9399%  ... 12.5915% 0.0000% 12.4035% 1.1702% 10.5504% 3.2020% 0.0000%   \n",
       "1 1.2312%  ... 14.2317% 0.0000%  6.9798% 0.0000%  8.0211% 4.2045% 0.0000%   \n",
       "2 0.2436%  ... 14.8324% 0.0000%  4.4942% 0.0000%  6.6988% 4.5286% 0.0000%   \n",
       "3 0.0000%  ... 15.1614% 0.0000%  2.5375% 0.0000%  5.6100% 4.7010% 0.0000%   \n",
       "4 0.0000%  ... 15.3321% 0.0000%  0.5110% 0.0000%  4.4575% 4.8411% 0.0000%   \n",
       "\n",
       "      TXT       VZ    ZION  \n",
       "0 0.0000% 12.5853% 2.2369%  \n",
       "1 0.0000% 12.7504% 2.2460%  \n",
       "2 0.0000% 12.6424% 1.9247%  \n",
       "3 0.0000% 12.4571% 1.5950%  \n",
       "4 0.0000% 12.1574% 1.2390%  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "points = 50 # Number of points of the frontier\n",
    "\n",
    "frontier = port.efficient_frontier(model=model, rm=rm, points=points, rf=rf, hist=hist)\n",
    "\n",
    "display(frontier.T.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqEAAAGoCAYAAAB2RTwLAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAACUp0lEQVR4nOzdd3gc1dXH8e9Zdbl3XLHBNgbbuAkwvYYWagIJhBICKaQ3QslLAiEhhBQghCSUQIBA6D0YCBB6KLGMjQvGBRvjgo3cZFlde94/ZiSvdK+ktdrsas+HZx5pp975eWzu3rlzR1QVY4wxxhhjulIs6gIYY4wxxpjMY5VQY4wxxhjT5awSaowxxhhjupxVQo0xxhhjTJezSqgxxhhjjOlyVgk1xhhjjDFdziqhJuOIyK9EpEREPgk/nyoiH4tImYhME5GFInJYEvspE5HdOru8URKRZ0Tky1GXI1OJyJ0i8qsO3N/LIvLVjtrfTh67zdeSiIwK/75ldXS5wv3fJyKndMJ+3xGRiR29X2O6C6uEmm5HRFaKSEX4P6366aZw2Ujgx8BeqrpLuMnvge+oak9VfVdVJ6rqy60dJ1z/ww4ob6sVDRFREdmecD5b2ntczzGuFJF7Euep6nGqelcH7X90eB5zmswfKCLVIrKyI47T0UTkpyKyIsx9tYg8kLAsskpdRwv//GtEZFs4LRGRm0RkaEfsf2eupfDv8FEJ264K/77VdURZmhxrb2AK8ET4+bzwOr2uyXqnhPPvFJF8EdkiIkd49ne9iDwcfvw9cFVHl9mY7sIqoaa7OjH8n1b99J1w/q7ARlXdkLDursDCri/iTpuScD59my4UkewIyuTVSll6iMikhM9fAlZ0cpHaJGy5Owc4SlV7AkXAi9GWqnUSaMu/7w+oai+gP3AqsAtQ3FEV0RT1DeBebfzmluXAF5tcx+cCSwBUtRJ4IJzXIGypPROor2w/CRzezfMzps2sEmoyRtiy8jwwLGzVuk9EyoAsYJ6ILA/Xa2iFEZGssCVsedg6VBy2pta3To4Nf88Tkd+LyCoRWS8iN4tIQbjssLAF7cciskFE1onIV8JlXwfOAi4Oy/TUTpxPfcviBSKyCviPiMRE5HIR+Sg81t0i0qfJ+l8Oy1kiIv8XLjsW+CnB/3jLRGReOL9RS5+InC8i74vIZhF5TkR2TVimIvJtEVkKLG2h6P8AEm/Lngvc3eTchonIIyLyadgK+b2EZfuKyJthS9S6sLUut0k5LhSRpWE5/ywikmyuTewDPKeqywFU9RNVvTU8ztXAwcBN0ri1/Y8SdO8oDa+XgxPKdqWIPBj+uWyToOtHUcLyaSIyJ1z2AJCfsKyfiPwrzGRz+PuIhOUvi8jVIvIGUA7sJiKfEZHFIrI1LF9SOahqjaouBL4IfEpw96D+OCeIyNww//9K0JKIiFwqO1oA69f9o4jcmFC+r4a/7y4i/xGRjeF1eK+I9A2X/QMYBTwV5npxwrWbHa4zTESeFJFNIrJMRL6WbMYexwGvNJn3CTAfOCbcZ3/gAIJKZb27gM+LSGHCvGMI/r/6TJhjJVAMHN3C8Y3JWFYJNRlDVV8g+B/O2rA18cywdQuCVsbdPZv9iKBl43igN3A+wf/gm7oWGA9MBcYCw4GfJyzfBegTzr8A+LOI9AsrNPcCvw3LdGIbTu1QYE+C/wGeF06HA7sBPYGbmqx/ELAHcCTwcxHZU1WfBX5N0BLWU1WnND2IBH3mfgp8DhgEvAbc12S1U4D9gL1aKO89wBkSVPD3BHoBbyccJwY8BcwjyOtI4Acicky4Sh3wQ2AgsH+4/FtNjnECQQVyCvCFMJu2eAs4V0R+IiJFktAnUVX/jyCD7zRpbf8fwXXQH/gn8JCI5Cfs8yTgfqAvQaWmvvKaCzxOUEnvDzwEfD5huxjwd4KW+1FABe6f7TnA1wky3Qo8AlxOkNVy4MCdOfnw9vcTBJVtRGQ6cAdB6+EA4BbgSRHJI7gWjheR3uG6WQTZ/9OzawGuAYYRXLsjgSvDY54DrGLH3Yzfera/D1gdbn8a8GsROTJhuTdjpxAiPYAxwAeexXezo6XzjDCHqoRs/gusI/j7UO8c4J+qWpsw732C69AY04RVQk139XjYUlM/fa31Tby+Clyuqh9oYJ6qbkxcIWxl+xrwQ1XdpKrbCCp0ZySsVgNcFbYwzQLKCCqCO2NOwvncmDD/SlXdrqoVBK2q16nqh6paBlxGUOFLvK34C1WtUNV5BBW9ZP8H+Q3gGlV9P/yf7K+BqYmtoeHyTWFZmrOa4H/6RxG0iN7dZPk+wCBVvUpVq8N+t7cR5qmqxar6lqrWqupKgorQoU328RtV3aKqq4CXCCqFO01V7wG+S1CJfQXYICKXtraNqm4My/cHII/Gf9avq+qssIL3D3bkPxPIAW4Ir5OHCSq09fvdqKqPqGp5eI1d7TnvO1V1YfjncxywSFUfVtUa4AaCFr6dtZagUgzBdX6Lqr6tqnVhH88qYKaqfgTMIfgiAnAEUK6qb3kyWqaqz6tqlap+ClznORcvCe5EHARcoqqVqjoX+BtBBbBecxk31Tf8uc2z7DHgMAnuJDit9aGGimpY+T6ZHbfi621LOI4xJkHK9CEzpoOdErZ8ttdIghaklgwCCgn6ztXPE4Lb/PU2NmkdKSdopdwZ01V1WcMBREaHv36csM4w4KOEzx8R/D0fkjAvsSKyM+XYFfijiPwhYZ4QtFbWH/NjZyu/uwlabA8ADgHGNTnOMGn88FUWQasjIjKeoNJSRJB7NsEtz0RJnaME3THq7RVWWhtR1XuBe0Ukh6CCda+IvKuqzzWzzx8TfHkZBihBC/rAFsqWH35JGAasadI3seHPMrztez1wLNAvnN1LRLISHthpei00fFZVFZFk/3wSDQc2hb/vCnxZRL6bsDw3PBYErZ5nEvz5fgl/KygiMhi4kaCFtRdBg8jmJMszDKj/slfvI4LroZ434yZ/BwG2hD97AZWJC1S1QkSeJmxJVtU3ROS4JtvfDVwhIsMJvqgsU9V3m6zTK+E4xpgE1hJqTMs+Bny36ROVENwanaiqfcOpT8Kt/tZo66skvf1agopCvVFALbC+A8rxMfCNhHPsq6oF4W3JZPdR7xHgs8CHYQta0+OsaHKcXqp6fLj8r8BiYJyq9iboItCmPp/a+OE1pwLaZN0aVX0IeA+of7Cq0fmG/T8vIbgN3U+DB8i2Jlm+dcDwJv1XRyX8/mOCFtX9wvM+pP6wicVssr+RCWWTxM/JCLtGnEj4BYDgz+bqJn82hapa3y3jIYLWwxEEDzZ5K6EEt+IV2Ds8l7NbOI+m1gL9RaRXwrxRwJqdOTcAVd1O8CVzfDOr3E2Q+z+a2X4VQTZnEbTE+lpL9yS442CMacIqoca07G/AL0VknAT2FpEBiSuoapzgdvH1YQsPIjI8oQ9ja9YT9N/sCPcBPxSRMSLSkx39PJu2ADVXjtHS/FPVNwOXSTjuoYj0EZHT21LI8H/+RxC0GDb1DlAqIpeISEHYd3SSiOwTLu8FlAJlIjIB+GZbypAMCYbr+ayI9JLgoa/jgIns6MPa9M+uF0Gl/1MgW0R+TtASmow3w22/JyLZIvI5YN8m+64AtoQPylzRyv6eBiaKyOfCltbvEfRNbpWI5IT9de8Lt6kfrug24EIR2S/8+9CjPh+A8Nb6ywR9V1eo6vvNHKIXQZeULWEr4k+aLG/274Sqfgz8F7hGgqGS9iboZ31vMufmMYvmuwK8AnwG+FML298FfIegv22jMoR9ZWcQPBBpjGnCKqGmu6p/srZ+eqyN+7kOeBD4N0HF53agwLPeJcAy4C0RKQVeIPk+n7cDe4V9PR9vYznr3UHQavMqwbBHlQR9GpPxUPhzozQZyxNAVR8jeADr/vAcFxD0O2wTVZ2t4VPnTebXEbS+TSU4hxKCLwN9wlUuIrjVu42gUvRA0310oFKCltZVBLdUfwt8U1VfD5f/EThNgqfVbwSeI3gyegnBLeJKkuyioKrVBA+5nEdwa/qLwKMJq9xAcO2VEDww9Wwr+ysBTgd+A2wk6PLwRivF+GLYRWELwQM9G4EZqro23Odsgn6hN4VlXBaWN9E/Cfr7NtcKCvALYDpBK/HTND5PCFpKLw//Tlzk2f5MYDRBq+hjwBWq2taK3q3AWU1aoIGgC4Oqvqiqmzzb1XuYoHvEi6q6rsmyk4CX6/MzxjQmjbsfGWOMMZlFRP4JPKiqj3fwft8GLlDVBR25X2O6C6uEGmOMMcaYLme3440xxhhjTJezSqgxxhhjTIYQkTskeKOet5tI+NDpUyIyT4I3jn2ls8pilVBjjDHGmMxxJ8F4w835NsGLLqYAhwF/kIRXI3ckG6x+Jw0cOFBHjx4ddTHapK6ujqysrNZXzDCWi8sy8bNcXJaJn+XiSvVMiouLS1R1UBTHPubwHrpxU13rK7ai+L2q51S1pQomqvpqwstOvKsQvAhDCF70sYlg+LgOZ5XQnTR69Ghmz54ddTGMMcYY04FEpOmLM7rMxk11vPPcqNZXbEXW0KUTRCSxknKrqt66k7u5iWCItrUEY/p+MRwPu8PZ7fgMUlzc9M2GBiwXH8vEz3JxWSZ+lovLMmmeAvEO+A8oUdWihGlnK6AQvIJ2LsErcqcCN4lIsi/d2ClWCc0gM2bMiLoIKclycVkmfpaLyzLxs1xclkna+ArwaPiyhmUELw2Z0BkHskpoBrFvoX6Wi8sy8bNcXJaJn+XiskxaotRpvN1TB1kFHAkgIkMI3v73YUftPJENVr+TioqKtGmf0JqaGlavXk1lZWVEpTImPeXn5zNixAhycnKiLooxJsOJSLGqFkVx7OlT8vSNZ4e1ez+Fw1a2eg4ich/BU+8DgfXAFUAOgKreLCLDCJ6gHwoI8BtVvafdhfOwB5M6wOrVq+nVqxejR4/G8/rhlFFeXk5hYWHUxUg5lourKzJRVTZu3Mjq1asZM2ZMpx6ro8yfP5/JkydHXYyUYpn4WS4uyyQ1qOqZrSxfCxzdFWWx2/EdoLKykgEDBuxcBXTrVpg4MfjZRfLz87vsWOnEcnF1RSYiwoABA9LqDsL48eOjLkLKsUz8LBeXZdKyDnowKa1YJbSD7HQL6L/+BYsWwdNPd06BPKqrq7vsWOnEcnF1VSapfOfAZ9WqVVEXIeVYJn6Wi8syaZ6i1Gn7p3RjldCo3HVX459dwPrd+VkuLsvEb8iQIVEXIeVYJn6Wi8syaVkcbfeUbqwSGoWyMnj11eD3V16B7dvbvUsR4Zxzzmn4XFtby6BBgzjhhBMazWuLl19+mT59+jBt2jQmTJjARRdd1LDsySef5De/+U2z295555185zvfSeo4J598Mvvvv3+zy1euXMmkSZMAmD17Nt/73veSPIPGbrjhBsrLyxs+J5PLyy+/3CjLxPnNZZPs8TtaVlYWU6dOZdKkSZx++uk7day5c+cya9ashkyqqqo46qijmDp1Kg888ECz2x122GENL3E4/vjj2bJlS7vOIVV11/NqD8vEz3JxWSamKauERuGZZyA3fA1rbm7wuZ169OjBggULqKioAOD5559n+PDhjdaJxdr+x33wwQfz7rvv8u677/Kvf/2LN954A4CTTjqJSy+9tO0FD23ZsoU5c+awZcsWVqxY0er6RUVF3HjjjW06VtNKYHtygeazSfb4ydiZLxAFBQXMnTuXBQsWkJuby80335z0MeorofWZvPvuu9TU1DB37ly++MUvJrWfWbNm0bdv36TLm06s/7DLMvGzXFyWSfMUqEPbPaUbq4R2pnffhWuu8U/btgXrbNsGv/61f513392pwx133HE8HfYxve+++zjzzB0PwL3zzjsccsghTJs2jQMOOIAPPvgAgOuuu47zzz8fCJ5cnDRpUosVpIKCAqZOncqaNWuAxi2dDz30EJMmTWLKlCkccsghzrZPP/00+++/PyUlJc6yRx55hBNPPJEzzjiD+++/v2F+cXExU6ZMYf/99+fPf/5zw/zElskrr7yS3//+9w3LJk2axMqVK9m+fTuf/exnmTJlCpMmTeKBBx7gxhtvZO3atRx++OEcfvjhQFBh33///Zk+fTqnn346ZWVlADz77LNMmDCBgw46iEcffbTZTJrL5t///rezX9/xe/bs2bCPhx9+mPPOOw+A8847jx/96EccfvjhXHLJJZx33nl873vf44ADDmC33Xbj4YcfbrVMBx98MMuWLWPTpk2ccsop7L333sycOZP33nuvIbuvf/3rHH300Zx77rn8/Oc/54EHHqCoqIgHHniAs88+m7lz5zJ16lSWL1/Oiy++yLRp05g8eTLnn38+VVVVzjFHjx7d8Gd83XXXMWnSJCZNmsQNN9zQanmNMSZTZeLteBuiqTOVlMAvfgHV1dDSAxjz5gVTPdWghbRo54YrO+OMM7jqqqs44YQTeO+99zj//PN57bXXAJgwYQIvvPACPXr04IUXXuCnP/0pjzzyCD/4wQ847LDDeOyxx7j66qu55ZZbWhyaZ/PmzSxdutRbybzqqqt47rnnGD58uHPb5bHHHuO6665j1qxZ9OvXz9n2vvvu44orrmDIkCGcdtppXHbZZQB85Stf4U9/+hOHHnooP/nJT3Yqj2effZZhw4Y1VMy3bt1Knz59uO6663jppZcYOHAgJSUl/PrXv27I5tprr+W6667j4osv5mtf+xr/+c9/GDt2bFKtgInZlJSU8Ktf/crZ789//vNGx2/NkiVLeOGFF8jKyuK8885j3bp1vP766yxevJiTTjqJ0047rdlta2treeaZZzj22GO54oormDZtGo8//jj/+c9/OPfcc5k7dy4QVPRff/11CgoKuPPOO5k9ezZ/+MMfyMvLY8iQIfz+97/nX//6F5WVlRx22GG8+OKLjB8/nnPPPZe//vWv/OAHP/Aev7i4mL///e+8/fbbqCr77bcfhx56KNOmTWv1vFNVOj3J31UsEz/LxWWZmKasJbQzfeYzMH8+7LEH5OdDPL5jSpQ4Pz8fJkyABQuC7XfC3nvvzcqVK7nvvvs4/vjjGy3bunUrZ511FpMmTeKHP/whCxcuBIJb0XfeeSfnnHMOhx56KAceeKB336+99hp77703u+yyCyeccAK77LKLs86BBx7Ieeedx2233UZdXV3D/Jdeeolrr72Wp59+2lsBXb9+PcuWLeOggw5i/PjxZGdns2DBArZu3cqWLVs49NBDARr1eU3G5MmTeeGFF7jkkkt47bXX6NOnj7POW2+9xeLFiznwwAOZOnUqd911Fx999BGLFy9mzJgxjBs3DhHh7LPPbvY4vmzeeustFi1a5Ox3Z51++ulkZWU1fD7llFOIxWLstdderF+/3rtNRUUFU6dOpaioiFGjRnHBBRfw+uuvN+R3xBFHsHHjRraGw4OddNJJFBQUNNpHdrb7/fSDDz5gzJgxDcOsfPnLX+bV+r7NHq+//jqnnnoqPXr0oGfPnnzuc59r+FKUrrprN4P2sEz8LBeXZdI8BXs63nSCceOCVs4LLoDWBv8uKICvfhXmzoWxY9t0uJNOOomLLrqo0a14gJ/97GccfPDBLFiwgKeeeqrRN9KlS5fSs2dP1q5d2+x+Dz74YN577z3mz5/PX//614ZWtEQ333wzv/rVr/j444+ZOnUqGzduBGC33XZj27ZtLFmyxLvvBx54gM2bNzNmzBhGjx7NypUruf/++1HVpIbwyc7OJp5Qsa8/t/Hjx1NcXMzkyZO57LLLuOqqq5xtVZUjjjiCuXPnMnfuXBYtWsTtt98OJD98kC8bVeUzn/mMd79NJR6naUtBjx49Gn3Oy8trVHaf+j6hc+fO5U9/+hO5ubnedeuP2/QYELwFrKmdfbtad3wbW3MV/0xmmfhZLi7LpGXxDpjSjVVCu0JuLtx4IzzwADT3EEwsBg89BH/8446Hltrg/PPP5+c//7nzVoqtW7cyatQoIOjHmTj/+9//Pq+++iobN25stZ/h+PHjueyyy7j22mudZcuXL2e//fbjqquuYuDAgXz88ccA7Lrrrjz66KOce+65DS2wie677z6effZZVq5cycqVKykuLub++++nb9++9OnTh9dffx2Ae++911um0aNHM2fOHADmzJnT8GDT2rVrKSws5Oyzz+aiiy5qWKdXr15sC/vkzpw5k7feeotly5YBwZuClixZwoQJE1ixYgXLly9vKGNrErOZOXMmb7zxhrPfpseHYNiS999/n3g8zmOPPdbqcdrikEMOacjv5ZdfZuDAgfTu3dtZr75suZ5rcMKECaxcubLhnP7xj380tFI3d8zHH3+c8vJytm/fzmOPPcbBBx/cQWcUjfq/Q2YHy8TPcnFZJqYpq4R2pZwc8LQ6AcH8DhibccSIEXz/+9935l988cX89Kc/5cADD2x0q/yHP/wh3/rWtxg/fjy33347l156KRs2bGjxGBdeeCGvvvqq8xT7T37yEyZPnsykSZM45JBDmDJlSsOyPfbYg3vvvZfTTz+9oWIHwbBLq1atYubMmQ3zxowZQ+/evXn77bf5+9//zre//W32339/55ZxfUve5z//eTZt2sTUqVP561//2nC7eP78+ey7775MnTqVq6++mssvvxyAr3/96xx33HEcfvjhDBo0iJtvvpkzzzyz4aGdxYsXk5+fz6233spnP/tZDjroIHbdddcWM2maTVlZGXfeeaez36bHB/jNb37DCSecwBFHHMHQoUOTOs7OuvLKK5k9ezZ77703l156KXc1Mz7t4YcfzqJFi7xDMuXn5/P3v/+d008/ncmTJxOLxbjwwgubPeb06dM577zz2Hfffdlvv/346le/mtb9QYFmW/MzmWXiZ7m4LJPmaQc8GZ+OT8dLd7xl1pmKioq0fjzEeu+//z577rln6xufcw7ce2/w4BFAz57BmKEQPLh09tlw990dXOLu6ZFHHuHJJ59stjJl0kfSf3+MMaYTiUixqu7cE8EdZO+9c/TJWa0/rNqaMSM/iewc2sJaQrtKbS088URQAc3Ph+HD4eabYdiw4LMqPP44JLRSdrTtHTAofip48skn+b//+z++8Y1vdMj+uksuHcky8SsuLo66CCnHMvGzXFyWiWnKKqFd5bXXoKIieDjp5JNh8WI466zg50knBfMrKoL1OonvAZR0dNJJJ7F48WIOOOCADtlfd8mlI1kmfjNmzIi6CCnHMvGzXFyWSfMUezDJtEOr3RoeegiysuDWW+H++4Nb8QC9egUPLN1yS7D8wQc7rYzWuuVnubi6KpN06w5kLTkuy8TPcnFZJi0R6jpgSjfWJ3Qn+fqErlixgl69ejFgwIDmh/V55x0YOBB22635nX/4YTDA/b77dmCJjUlNqsrGjRvZtm0bY8aMibo4xpgMF2Wf0El75+ojT7e/T+iEUevSqk+ovTGpA4wYMYLVq1fz6aefNr9Sr15QVQXvv9/yznr1an2dNqqurvYOvZPpLBdXV2WSn5/PiBEjOv04HWXevHmNRn0wlklzLBeXZWKaskpoB8jJyUmLlpza2lrvm3AyneXiskz8Jk6cGHURUo5l4me5uCyTlqXj7fT2sj6hGaR+kHHTmOXiskz8LBeXZeJnubgsk+YpZGSfUKuEZpB0uu3ZlSwXl2XiZ7m4LBM/y8VlmZimrBKaQUpKSqIuQkqyXFyWiZ/l4rJM/DI9l5q6Ol6cu5SbnnqDx99cQHlVdcZn0pq4SrundGOdvjJIz/phoUwjlovLMvGzXFyWiV8m5LKtogpF6V2Q32h+aXkl513/AJ9s3kZ5VQ0FuTnc8MRr/Pmrx0ZU0tRXfzs+01glNIPU1NREXYSUZLm4LBM/y8Vlmfh151xWb9zKT//5LPNXfQLAhOGDueZLxzJ6cD8A/jrrTT7+dCs14RsAK6prqKyp4e7n/8e1Y1P/IV7TdawSmkHi8XR8n0Lns1xclomf5eKyTPzSOZet5ZU8WbyI5Rs2MXHEYD47bU8Kc3MAqK6t5Zwb72dTWQXxcJzxhR9/wjl/up/nLv8qhXk5PFf8QUMFtJ4qrNu8lbKKKnoW5HX5OaU6RajLwB6SVgnNIIWFhVEXISVZLi7LxM9ycVkmfumay4pPN/GlP99PdW0dlTW1/GtuDn954S0e+O6XGNy7J/+Zv5zy6pqGCigEFcyqmjqem7eEU/ed2OxLW7aU1zb/QheTln062yvzqt0ZbNOmTVEXISVZLi7LxM9ycVkmfqmay7ot2/jhff+i6KqbOODXf+UPz75GVU1tw/IrHn6ebZVVVIbzKqpr2FhWzu/+9SoAazaVUp2wfr2K6hrWbNwKwGf33ZPc7KxGy2MxYd/dBtMj314M4mNDNJlub9iwYVEXISVZLi7LxM9ycVkmflHmsrm8gg9LNlHd5Jb4tsoqvvDXf/L8omWUV9ewpbySe956l2/d8wQQ3Gp/96N1NH2bd11cefn9DwHYc8Rgcj0vsijMy2HPEYMBuPC4mYwdNpDCvByyYkJhXg4DehZy1rEHdcLZmnRmt+MzyIoVK9hrr72iLkbKsVxclomf5eKyTPyiyKWsqppLH3+WV5euJDsrRkyES44+hNOnTwbgsTkL2V5d3ehWelVtHe+uWsvidZ+y++D+xEQaLa+XkxW0bM4cN4oxQ/qzdF0J1bVBJTcnO8bQfr05dK/dACjMy+WeH5/J20tW8cHqTxk2oDeHT96dpUs+gGFDOjuGNCXUaea1C1olNINMmDAh6iKkJMvFZZn4WS4uy8Svs3LZXF7B/HXrGdCjgL2GDG7Ux/Injz7DG8s/orqurqEV9NfPvsywPr05cPddee/jTxpusyeKibDkk0+ZMHQQh+25Gy+//yG1CQ9W5WZncdKMPYN1Y8Id3zqdm//9Fv8qfh9V5bhpE/jmMTPJztpRiYrFhP0n7Mr+E3ZtmGfXSvMUiGfgzenMO+MMNnfu3KiLkJIsF5dl4me5uCwTv87I5c+vv8XBf7qNHzz+NGf94yGOv/Vu1pVuA6CkbHtDBTRRRU0tt73xPwDGDhlAXpO+mvVGDegLwJWfP4pRA/tSmJtDfk42Bbk57DV8MN875sCGdQvzcvjRiQfznyu/zku/+AYXn3IovZJ44t2uFdOUtYRmkOnTp0ddhJRkubgsEz/LxWWZ+LUll7p4nDlr17K9upoZw4fTK29Hxe6lZR9y65v/a9TKuXLTZi586AmeuOBsNm6vICcr5lRCAT4pLQPg9KLJ3PHabKpqd6yTkxVj9IB+TBk5FIB+PQp44ofn8s6HH7OqZAvjdhnI1F2HdshT7XattCwdHyxqL2sJzSDFxcVRFyElWS4uy8TPcnFZJn47m8uST0s4+Jbb+Oojj/GDp2Yx8y+3cO+7cxuW3/XOu1Q0uZVep8qKjZtZsXEzu/bvi9uTE7Jjwr6jg3e2D+hZyD1f+yJ7j9iFmAjZWTGO3HMst5//+UaVzFhMmDl2FF+YuTfTRg/rsGGV7FppnmrQJ7S9U7qxltAMMmPGjKiLkJIsF5dl4me5uCwTP18u5TU1fLRlC7v07Em/goKG+XXxOF9+6GE+3V7eaP1rXn6VvYfuwuRddmFLRYX3ONmxGKWVlYwZ0I8fHHEg17/4ekNlNUuEgtxcLjx434b1x+8ykPsvPJPq2lpiEmvUj7Oz2bVimkq/arNpszlz5kRdhJRkubgsEz/LxWWZ+CXmoqr88c3/UnTLX/niQw+w/99u5YfPzqKqNqgsvrN6NeWeB4aq6+r459z3ADhqj7HkZbn9ORXYc8ggAM7dbxp/+PzxTB85jBF9e3PK1L147BtnMaxPb2e73OzsLq2Agl0rrYkj7Z7SjbWEZpCpU6dGXYSUZLm4LBM/y8Vlmfgl5vLwwoXcWjybytodFc3nli2jR04uvzryKMqqqr3Vh7hqQwvol4um8dh7i/i0bDuVtbXERMjNyuLKY45oNG7nEXvszhF77N5Zp9Uudq00LxisPvPaBTPvjDPY4sWLoy5CSrJcXJaJn+XiskyC2+y/ffM1Zv79Fva742aufv1l3lu4sGH5X2e/Q0Vt45bOytpaHlm0kKraWopGDHfetQ5QkJPN0ePHAdArP48nLjib7x+yPzN3HcmJEydw7zmnc/LkPTv35DqQXSstsT6hppsbM2ZM1EVISZaLyzLxs1xcmZ5JXJUzH3uQDzZ+SlVYkbz7vbm8278/D0ycSFYsxqZm+nPGNajA9iso4AcHHcCNb7xJZW0tSlABHT9wIJ+dsEfD+j3zcrlgZhEXzCzqilPrcJl+rRhX+lWbTZutXbs26iKkJMvFZZn4WS6uTMlkQcl6/rV8Mcs2b2w0/7+rV7Fs88aGCihAdbyOfjV1/Oej4FWX04cO895uH1BYQN/8fAC+tu8+/P30z3PinhM4dMxorjjyCP55xhfI9fQDTVeZcq20Rf1g9e2d0o21hGaQ/v37R12ElGS5uCwTP8vF1d0zKa2q4rxnHub9jSVkiVAbj3Pg8FH85eiTyMvKZv6GTxoeMEq0pGI789d/wmfGjOWSgw7mnTWrqaqtpS58JWZ+dja/OPyIRsMfFY0YTtGI4V12bl2tu18r7VWn6fdgUXulX7XZtFl5eXnrK2Ugy8VlmfhZLq7unsnPXn+B+Z9uoKK2hrKaairranljzUfcWPwmACN69SE/223PGZqbz8jefQHYY+BAnvzS2Zy4xwRG9+3LoaNH84/PncZndh/blacSue5+rZid12mVUBHJF5F3RGSeiCwUkV+E8/uLyPMisjT82a+Z7Y8VkQ9EZJmIXJowv9ntReSycP0PROSYhPkzRGR+uOxGCb96ish3RWSBiMwSkdxw3kEicl1n5RKlWMy+c/hYLi7LxM9ycaV7JrXxOE99+D7feOExfvTK0/xv/epGy2Z9+AE18cYPDVXW1XHf+8HQSUfvNpaC7BxiCS2aAhATPjtuR3/O3fr147pjj+M/553P30/5HDOGDevU80pF6X6tdCZFqCPW7inddGaJq4AjVHUKMBU4VkRmApcCL6rqOODF8HMjIpIF/Bk4DtgLOFNE9goXe7cPl58BTASOBf4S7gfgr8DXgXHhdGw4/6vA3sC7wDFh5fRnwC87KIOUkpOTE3URUpLl4rJM/CwXVzpnUheP8+XnHuLi157l2Y+W8uiyhZzzzIPcNDdo5ayNxxtunzdVP9xSXnY2D592JlMG70JOLEZOLMakwUP46cGHU5jG2XSGdL5WukJcY+2e0k2nlVgDZeHHnHBS4GTgrnD+XcApns33BZap6oeqWg3cH25HC9ufDNyvqlWqugJYBuwrIkOB3qr6pqoqcHeTY+YAhUANcA4wS1U3t/W8U1lZWVnrK2Ugy8VlmfhZLq50zuTfq5YxZ8NaymtrgOB/UBV1tdw4979sKC8jPzubiQMGO9vFRDhkxOiGz7v26cujp3+J/53/Td45/0Ke/MLZ9Jb0qxB0tnS+Vkzn6NS/JSKSJSJzgQ3A86r6NjBEVdcBhD/dv+EwHPg44fPqcB4tbN/cNsPD3337+j3wFjAIeAP4MvCXls5p3bp1TJ06lSlTpjBlyhSuv/56li9fTkVFBYsWLSIejze8FaL+Pblz5swhHo+zaNEiKioqWL58OZs3b2bNmjWsW7eOkpISVq5cSVlZGYsXL6a2tpZ58+Y12kf9z/nz51NVVcXSpUspLS1l1apVbNiwgQ0bNrBq1SpKS0tZunQpVVVVzJ8/v9G269evB2DevHnU1tayePFiysrKWLlyJSUlJaxbt441a9awefPmtDmn+p/tOaf6XLrTObX3z2ngwIHd7pw64s/Jl0u6n1N7/5y2bNmSsuekqjz+yot85onb+dkj9/Ct/zzKf+e923BOby3/gN1iuUzL7cWo7HwOye9L31g2ny0YwH/XfsScOXO45tCjOa/fUHJjWXypzxAKs7L5XJ/B/GT6TOecasrK2PLJesrKyigtLU2pP6dUuPby8/NT/pyiUj9Yfabdjhdt5lZDhx5EpC/wGPBd4HVV7ZuwbLOq9muy/unAMar61fDzOcC+qvpdEdni215E/gy8qar3hPNvB2YBq4BrVPWocP7BwMWqemKTY14BzCW4Fs4lqND+WFXjiesVFRXp7Nmz25lINBYvXsyECROiLkbKsVxclomf5eJK5Uz+9N4b/GX+21TUBS2dAhRm5zLrxPPYtVc/rnjzBe5e9C5xGv9/sGdOLjcedgJHjgoeHFq/vYx/LJzLB5s+ZergoXxpryn0yy9oerhGUjmXqKR6JiJSrKqRDMI6ZnJPverRSe3ez7nj347sHNqiS6rNqroFeJmgL+b68BY54c8Nnk1WAyMTPo8A6gcYa2775rZZHf7u2xfhfoYB+6jqE8DlwBcJ+rQeuROnmfLGjs2sJzGTZbm4LBM/y8WVCpnUxuM0bVApr6nmL/PfaqiAQni7vbaGm94L+nyescfe3nE4syTGQcNHN3we0qMnF+17ELcdeyrfnj6z1QoopEYuqcYyaVkmjhPamU/HDwpbQBGRAuAoYDHwJMFtb8KfT3g2/x8wTkTGhE+tnxFuRwvbPwmcISJ5IjKG4AGkd8Jb9ttEZGb44NG5nmP+kuCBJIAC6seNDfqKdhsLE14jZ3awXFyWiZ/l4ooyk3c2rOLYp29jj/t/w6QHf8/Vc16gOhw0fuW2LWR5nsaOo8z+NOihtWf/wVwx80jysrLpmZNLz5xc+uXlc/exp5OX1b5htO1acVkmpqnOHKx+KHBX+IR6DHhQVf8lIm8CD4rIBQS3yk+HhtbIv6nq8apaKyLfAZ4DsoA7VLX+6v2Nb3tVXSgiDwKLgFrg26paP67GN4E7CSqYz4QT4XGnhdu/G866HZhPcDv+Fx2cSaSmTJkSdRFSkuXiskz8LBdXVJl8sGUDX3npgYaWzoq6Gu5dOoeSyu1cf8DJDCns2VAhbWpUz74Nv39pwhQ+O2YP3v7kYwqzc9hv6EhyYu1/S5FdKy7LpHmqpOW739urM5+Of09Vp6nq3qo6SVWvCudvVNUjVXVc+HNTOH+tqh6fsP0sVR2vqrur6tUJ873bh8uuDtffQ1WfSZg/OyzD7qr6HU24b6Oq76rqBQmfb1DViap6rKpWdVY+UajvtG0as1xclomf5eLq7EwqamvYUuW+e/3mhW9SFW/8pqLKulqeWbWYTyvKGJBfyGdGjiOvye32gqxsvjP5gEbz+uTlc/Su4zho+OgOqYCCXSs+lklLhHgHTOnGXtuZQWbMmBF1EVKS5eKyTPwsF1dnZbKlqoJL//cUr3yyHFBG9ejPNfucwPSBQRf/D7Z+StzzYG1eVjart29lUEFP/nDg8fzs7ed5YsUiRKB3bj6/2Oco9hkywtmuo9m14rJMTFOZ1/abwexbqJ/l4rJM/CwXV2dkoqp85dV/8sony6iJ11ETj7N8WwnnvXova7ZvAWBS/13IErflp6qull17BgOu5Gfn8LsDj2feGd/ntVMv5O3Tvs3xo7vm6Wy7VlyWSfOU4HZ8e6d0k34lNm1m30L9LBeXZeJnubjak4mqsq2mkrrGI+GxcMsnLCstoSbeeH5NPM49y4KKzIV77e88PFSQlc1pu+1N//zGz5QWZOcwuLBno1drdja7VlyWScsycZzQ9CuxabP6wYFNY5aLyzLxs1xcbc3k8Y/mcdCs69j/X79n36d+y02LXmm4vf5x2RayPG8cqonXsby0BIDdeg/ggaPOYd9BI8mNZTEwvwffmXQQvyg6pu0n04HsWnFZJqYp6xOaQcaPHx91EVKS5eKyTPwsF1dbMnlh7WKunDuLyvDJ9traam5f+l/iKN/b6zD27DuEmrj7ZHt+VjYzBu4YDnpi/124/zPntL3wnciuFZdl0jxFiGv6PVjUXtYSmkFWrVoVdRFSkuXiskz8LBdXWzL506JXGiqg9Srqarhz6VvUxuOM7tWfI4ePIz/hdnuWCD2y8zhj92ntLnNXsGvFZZm0rKtux4vIHSKyQUQWNLP8JyIyN5wWiEidiPTv0JMNWUtoBhkyZEjURUhJlovLMvGzXFy+TEqrK7ju/ed4bu1CVJWjhu7Fj/c6hn55PQBYW77Fu69araOstoq+uQVct9+p3P7BW9y7fDbltdUcNnQcF00+nD65rb+tKBXYteKyTFLGncBNwN2+har6O+B3ACJyIvDDxOEwO5JVQjPIli1b6N27d9TFSDmWi8sy8bNcXE0zqdM4577xNz7evoma8H0hs9a8x7ubVvH44d8lJ5bF+D6DKd74sbOvwuxceufkA5Adi/GNPQ/gG3se4KyXDuxacVkmzVMg3kVPt6vqqyIyOsnVzwTu66yy2O34DJKfnx91EVKS5eKyTPwsF1fTTF7fsJRPKrY2VEABajXOxqoyXvpkMQA/mnhko1vtAPlZOfxwryO69An2zmTXissyaYlQ1wETMFBEZidMX29ziUQKgWOBRzrqLJuyllBjjDE75YPStdy5/BVWlG1g35yRnNbjEEb1GAjAstL1VDXp7wlQXlfN0m3rOZqJzBg4ir8deBa/X/AiS0s3sEtBb76z56EcN2JiV5+KMSmhA1tCS1S1qCN2BJwIvNFZt+LBKqEZpbKyMuoipCTLxWWZ+Fku8L+Ny/nR7LupiteiKL3q4pz9xk3cPvMbjOs9lFE9BpCXlUN5XXWj7QqzchndY0DD56KBu3L/Yed3dfG7jF0rLssk7ZxBJ96KB7sdn1H69u0bdRFSkuXiskz8LBf4zYInqIzXoARjeq6RMirqqrlh8SwADttlD3rnFpCV8B7rWPhk+5FD94qkzFGwa8VlmbSsg27HdwgR6QMcCjzRYTv1sEpoBlm/fn3URUhJlovLMvHLlFwWbf2Y25Y9xz0rXuaTis0N8yvrqlldsbHRumO1LwDvbQ6G38mJZXPPQV/jwMHjyJIYWRJj/4G7c89BXyM/K6fLziFqmXKt7AzLpHmqQlxj7Z6SISL3AW8Ce4jIahG5QEQuFJELE1Y7Ffi3qm7vhNNtYLfjM8ioUaOiLkJKslxclolfd89FVbl20SM8/8lcquI1ZEsWd3z4PJfudRpHD51GTiybHMmmSnf0+ZwX+xSA3jk7hk4anN+bm/Y7m9p4HQrkxLK6+lQi192vlbawTFKDqp6ZxDp3Egzl1KmsJTSDLFmyJOoipCTLxWWZ+HX3XP63aSnPr58X3m6HGq2jKl7LbxY9TFlNBVkS4+SRReTFdrRfHFg3jPxYDmeNOcjZX3YsKyMroND9r5W2sExaVqexdk/pJv1KbNps8uTJURchJVkuLsvEr7vksr5yM4tLV1HZ5OGhFz6Z58wDyJIs3tm0FIDv73Echw+ZSG4smx7ZebySu5ZTRhZxxuj0HM+zs3SXa6UjWSbNUyCOtHtKN3Y7PoMUFxczY8aMqIuRciwXl2Xil+65bKsp58oFd7KodCXZkk2d1nH+bsdz2shDgeABIoHwkaPGYmGbRW5WNr+c+kV+ULWNTyq2sG3pOmbutW/XnUSaSPdrpTNYJqYpq4RmEPvL72e5uCwTv3TP5ZcL72bB1hXUah3V1ALw9w+fYWThIPYbsBfHDp3O8+vmUhlvPM5nXOPsO2Bco3kD8noxIK8X7DOyy8qfTtL9WukMlklLJC1vp7dX5p1xBisuLo66CCnJcnFZJn7pnEtJ1Vbe2/ohtQlvMgKojFfzwKqXAJjabzc+N/IA8mLZ5MSyyY/lkBfL4Zd7n0Vhdp53v+mcSWeyXFyWSfOCweql3VO6sZbQDGLfQv0sF5dl4pfquWyo3MR9q55h3pYP6JPTi8+POIqDBk0DYGvNdrIli5qwBTTRpqrSht+/Pf54Thi+D2+WLKYgK5fDBk+iT26PZo+Z6plExXJxWSYtq8vAdsHMO+MMNm/evKiLkJIsF5dl4pfKuWys2sL3372W/6x/h0+rNrOsbBU3LLmHB1Y9C8DIwsHexxayJYui/hMazdu1xyDO2PVgTh6xX4sVUEjtTKJkubgsE9OUVUIzyMSJ9l5mH8vFZZn4pXIuj65+kYq6KuLEG+ZVxat58ON/U15bSW4sm2+OPZm82I4B47Mlix7Z+Zy565FtPm4qZxIly8VlmTRPaf+t+HS8HW+V0AyybNmyqIuQkiwXl2XiF3UuNfEaZm+ax2ufvs2W6q2Nlr23dQl1Tfp7QlDR/Lj8EwCOHzaTq/f+Kvv2n8CYHrtwyoiD+Ns+P2FAXu82lynqTFKV5eKyTFoWJ9buKd1Yn9AMMmLEiKiLkJIsF5dl4hdlLku3reCa9/9EnDiqSp3W8fkRn+XUEccBMCRvACu3r3W2q4nX0j+vT8Pnaf3GMa3fOGe9trJrxc9ycVkmpqn0qzabNispKYm6CCnJcnFZJn5R5VIbr+U3i29ie105FXWVVMarqNFaHl3zDItLg9alz404irxYbqPtciSLyX3GMSivX6eVza4VP8vFZZk0TxXqVNo9pRurhGaQnj17Rl2ElGS5uCwTv6hyWVjqv9VeE6/hPxveAGCvPrvxnXFn0jO7kLxYLjmSzfR+e3HJnl/p1LLZteJnubgsk5ZlYp9Qux2fQWpqalpfKQNZLi7LxK8zc1lTsZbH1zzJh2UrGJI/mJOGncCE3nsAUB13X6UJoCiVdZUNnw8bXMTBg6axvnITvbIL6ZXT8pPtHcGuFT/LxWWZmKasEppB4vF46ytlIMvFZZn4dVYuq8o/5leLrqE6Xo2ilFSXsLRsGd/Y7asU9Z/BXr3HUxt3W0LzYnnsP6Dx2ItZksWwgkGdUk4fu1b8LBeXZdK84On4zLs5nXlnnMEKCwujLkJKslxclolfZ+XywKqHqIpXoQlvba+OV3PPR/9EVemRXch5Y75AbiyHWDjaZ34sjz167cZ+A6Z3SpmSZdeKn+XiskxaVoe0e0o31hKaQTZt2kS/fp33gEK6slxclolfe3PZVF2CqtI/dyAiO/6HsXz7h971t9VuY3vddnpm9+SoIQczrucYXtrwBtvrytm3/zRm9NubmETblmDXip/l4rJMmlf/2s5MY5XQDDJs2LCoi5CSLBeXZeLX1lzWVnzM31b8iY1VnwLQP3cgF4z5DiMKdwWgd3ZvKuoqnO1ixMiP5Td83rXHCM4b88U2laGz2LXiZ7m4LBPTlN2OzyArVqyIuggpyXJxWSZ+bcmlqq6S65b8ik8q11KjNdRoDeur1nH90qupqCsH4MRhx5PbZHil3Fguhww6hOxYarcV2LXiZ7m4LJOWBH1C2zulm/QrsWmzCRMmtL5SBrJcXJaJX1tymbtltnd4pbp4HXM2vw3AQQMP5MShnyU3lkt+LJ9syWa//vty5qgvtLvMnc2uFT/LxWWZtCyOtHtKN1YJzSBz586NuggpyXJxWSZ+zeWiqqwoe5/Zm15idXnj/p1bajZR4xliqVqr2FKzGQAR4aThJ3DTtBu4YuLl3Djter6621dSvhUU7FppjuXiskxMU6n/L5zpMNOnR/sUbaqyXFyWiZ8vl+2127hl+ZVsqt4AKKowqsc4zh9zKTmxPMb0GEtOLIeqeFWj7fJi+YzpMbbxvKw8hhUM7cxT6HB2rfhZLi7LpHn1b0zKNNYSmkGKi4ujLkJKslxclomfL5dHV9/Cp5VrqI5XUh2vokar+Gj7B/z7kwcBGNdzT0YWjiFHdvT5zJFchhWMZEKvSV1W9s5i14qf5eKyTFqWiX1CRVVbX8s0KCoq0tmzZ0ddDGNMCqjTWi6ff7a3z2ePrF5cMekOIHi95ksbnuXNja+iwMwBB3PE4GOdh5GMMdERkWJVLYri2AP3HKifvevkdu/n7v3uiOwc2iL9qs2mzebMmRN1EVKS5eKyTBr7tPJj3ix5nOfffIrttVsb5sc1TryZL/K1Wtvwe04sh6N3OZErJv6OKyf+jmN3OanbVEDtWvGzXFyWSfOCNybZu+NNNzZ16tSoi5CSLBeXZRJQVZ7/5A5mb3qGuMaJ9crmfx/czWkjL2Z8733IieUysnB3VpUvbbSdEGPP3pnR/82uFT/LxWWZtCwdn25vL2sJzSCLFy+OuggpyXJxWSaBVeULKd70LLVaTZxaBm6cRK1W8cjHv6U6HFz+tBEXkh8rJDvs85kjefTM7s1nh50TZdG7jF0rfpaLyzJpXv0bk6wl1HRbY8aMiboIKclycVkmgfc2v0SN7hheaWOf9wGISYzlZe+yZ58D2KVgFBfveSP/2/QS6ys+ZmThWGb0P5T8rMx4T7ZdK36Wi8syMU1ZS2gGWbt2bdRFSEmWiyuTMqnTWpZve5v5W55lY9WqRsvixAnaKAJ9yoLXbCqgCfN7Zvfh8MGncMau3+XAQcdlTAUUMuta2RmWi8syaVkmPh1vLaEZpH///lEXISVZLq5MyWRT1WoeWPUTauIVxDWocI7rdTDHD7sIkRiT+x7Koq2vU6PBGJ/b8zcAENc6du85LcKSp45MuVZ2luXiskxakKa309sr/arNps3Ky8ujLkJKslxcmZLJE6t/wfbazVTHK6jVKmq1mmXb3mDB1ucBGNNjCpP7HkqO5AFCfm1fsiWXk4Z/n7wMau1sSaZcKzvLcnFZJqYpawnNILGYfefwsVxcmZDJluq1bK1ZT+LtdoAarWTe5n8xue8xiAgnDP8O0/sfw5LS2WhOPtPHHESf3EHRFDoFZcK10haWi8syaZ6SmU/HWyU0g+Tk5ERdhJRkubi6UyZxrWNbzXrysnqRn9WrYX6tViPN/KNfq43f9T6sYBzDCsZRklVCn9yBnVredNOdrpWOZLm4LJOW2e14062VlZVFXYSUZLm4uksmS0v/w53LPscDKy/gzuWf55k1P6O6bjsAA3JHkRMrcLbJllwm9D7Mu7/ukktHskz8LBeXZWKaskpoBhk40FpwfCwXV3fIZF3FAl765LdUxkup1UriWsOq7W/z77VXASAS44Thl5Ij+WQRtNDkSD79ckcwvf+p3n12h1w6mmXiZ7m4LJPmZeo4oVYJzSCrV6+OuggpyXJxdYdM3t14H7XhU+316rSGNRVzKav5FIBRPabyld1vY7+BZzC5zzEcPfQHnD3mRnJj+d59dodcOppl4me5uCyTlmViJdT6hGaQsWPHRl2ElGS5uLpDJttqP/HOj0k222tL6JkTPFzUO2cwBww6O6l9dodcOppl4me5uCwT05S1hGaQhQsXRl2ElGS5uNIhE1Xl47JXeX71d5m16iss3PQPauIVDcuHFUwlRpazXVzr6Je3a5uOmQ65dDXLxM9ycVkmzVPa3wpqLaEmpU2ZMiXqIqQky8WVDpm8u/EvfLDlIWq1EoAt1R+yfNszHD/yDrJj+UzrfwZLSp+nOr4dJQ5AtuQzvf+XyI21bYzPdMilq1kmfpaLyzJpWSYO0WQtoRmkuLg46iKkJMvFleqZlNeW8P6WBxoqoAB1WkVZzVpWbPs3AD1zBvGF0beyR++j6ZE9iEF54zlil4spGnhOm4+b6rlEwTLxs1xclkkL1PqEmm5uxowZURchJVkurlTP5NOK98gihziNx/Os00pWb3+DcX1OAqBXzi4cMfSSDjtuqucSBcvEz3JxWSamKWsJzSD2LdTPcnGlSibVdaVsqpxPRe2njebnZ/VFm7zpKBCjMLvzhoFJlVxSiWXiZ7m4LJPmZeoQTdYSmkHsW6if5eKKOhNVZcHGG1he+gAxySGu1exSeDD7DP4VWbF8BhdMJS+rN7W1FSS+djNLctijz+c7rVxR55KKLBM/y8VlmbQsHSuR7WUtoRlk/vz5URchJVkurqgzWVH6EB+WPkhcq6iNlxHXaj7Z/jpzS34LBAPNHzX8T/TOGUWW5JMjPciRHhww5Gf0zdut08oVdS6pyDLxs1xclolpylpCM8j48eOjLkJKslxcUWeyZMvd1CU8dAQQp4qPy55m2qDLiEkOvXNHctKu97O1egU18XL6540nK5bbqeWKOpdUZJn4WS4uy6R59UM0ZRprCc0gq1atiroIKclycUWdSXV8q3e+apzahLFARYS+ebsxqGBSp1dAIfpcUpFl4me5uCyTlqlKu6d0Yy2hGWTIkCFRFyElWS6urshka9VCPimbhaIM7XkcffImNywbmD+dT8pfgyYPHxVkDyYn1qvTy9Ycu1Zclomf5eKyTExT1hKaQbZs2RJ1EVKS5eLq7EyWbrqRd9adw8rSO/mo9C7eWfdlPtj0h4blkwZ8n2wpRBreeCRkST5TB/0Ukei+7du14rJM/CwXl2XSsjjS7indWEtoBsnPz4+6CCnJcnF1Zibbq1ewsvTvxLWqYV5cK1lVei/Dep5Er9xx9M7djSNHPsAHm//Opqr36JUzmvH9vkK/vD07rVzJsGvFZZn4WS4uy6R5qpn5dLxVQo0xXWpDxUuoxp35ca3h0/L/0Ct3HAA9coYzffDlXV08Y4yJRDr26Wwvq4RmkMrKytZXykCWi6szM4mRg0gMbTLWvBBDJKfTjtsR7FpxWSZ+lovLMumeRCQXqB/64ANVrUl2W6uEZpC+fftGXYSUZLm42ptJaeW7rNr6ZypqVtArdxIj+36HHmEL55Aex7Bk83XONiIxdik8pl3H7Wx2rbgsEz/LxWWZtCQ9h2gSkcOAu4CVgAAjReTLqvpqMtvbg0kZZP369VEXISVZLq72ZLKp/BXmrz+HzRUvU1n7EZ+WP8PcdZ+jrGoBAPnZg5k44CpikkeWFJAlBcQkjz0H/IyCnOEddQqdwq4Vl2XiZ7m4LJOWpekQTX8AjlbVQ1X1EOAY4PpkN7aW0AwyatSoqIuQkiwXV1szUVWWb7qSeKOB5uPEtYIPN/2GvYfeA8CwXicysPAgPi1/GVAGFh5KXtaAdpe7s9m14rJM/CwXl2XSLeWo6gf1H1R1iexEvyprCc0gS5YsiboIKclycbU1k7hWUFm71rtsW/W8Rp9zs/oxvNepDO/1ubSogIJdKz6WiZ/l4rJMmqcET8e3d4rAbBG5XUQOC6fbgOJkN7ZKaAaZPHly6ytlIMvF1dZMYpJHrJkvwTmx/u0pUkqwa8VlmfhZLi7LpAUaDNPU3ikZInKHiGwQkQUtrHOYiMwVkYUi8koLu/smsBD4HvB9YBFwYbKnbZXQDFJcnPSXk4xiubhayqS69hM+LPkhcz7em7mr92XNluuJazUAIlns0utMYtJ4PMCYFDCiz9c6tcxdwa4Vl2XiZ7m4LJOUcSdwbHMLRaQv8BfgJFWdCJze3LqqWqWq16nq51T1VFW9XjVhEOhWdFolVERGishLIvJ+WJP+fjj/ShFZE9aw54rI8c1sf6yIfCAiy0Tk0oT5/UXkeRFZGv7sl7DssnD9D0TkmIT5M0RkfrjsRglfuSIi3xWRBSIyKxxiABE5SETcR3e7gRkzZkRdhJRkubiay6Q2XsqiT05kY/mT1GkpNfENrCu9hWWf7vjiO6bfxQzqcSJCLlnSk5jkM7z3eQztdVZXFb/T2LXiskz8LBeXZdKyrnpjUvjk+qYWVvkS8KiqrgrX39B0BRF5MPw5X0Teazole86d2RJaC/xYVfcEZgLfFpG9wmXXq+rUcJrVdEMRyQL+DBwH7AWcmbDtpcCLqjoOeDH8TLj8DGAiQQ3/L+F+AP4KfB0YF0713wC+CuwNvAscE1ZOfwb8soMySCn2LdTPcnE1l0lJ2UPUaRlQ1zBPqaS06r9UVAf9vWKSw/iB17DfyDeZMvQBZo58m9H9fhzp6zY7il0rLsvEz3JxWSbNUzrs6fiBIjI7Yfp6G4ozHugnIi+LSLGInOtZ5/vhzxOAEz1TUjrt6XhVXQesC3/fJiLvA8mOv7IvsExVPwQQkfuBkwn6GpwMHBaudxfwMnBJOP/+sBl4hYgsA/YVkZVAb1V9M9zX3cApwDPhPnKAQqAGOAeYpaqb23TSKc6+hfpZLq7mMimrmkNcK5z5QhblNe9TkDu+YV5OVh9ysvp0WhmjYNeKyzLxs1xclklLOuzBohJVLWrnPrKBGcCRQAHwpoi8paoNT5aFdTyAb6nqJYkbi8i1BPWyVnVJn1ARGQ1MA94OZ30nbLK9I/F2eoLhwMcJn1ezowI7pP7kw5+DW9lmePi7b1+/B94CBgFvAF8m6AfRrHXr1jF16lSmTJnClClTuP7661m+fDkVFRUsWrSIeDzOnDlzgB3f+ubMmUM8HmfRokVUVFSwfPlyNm/ezJo1a1i3bh0lJSWsXLmSsrIyFi9eTG1tLfPmzWu0j/qf8+fPp6qqiqVLl1JaWsqqVavYsGEDGzZsYNWqVZSWlrJ06VKqqqqYP39+o21feOEFAObNm0dtbS2LFy+mrKyMlStXUlJSwrp161izZg2bN29Om3Oq/9mec6rPpTudU3v/nObNm+c9p2wdT9n6o6mpGEr5xn2oLN2DytI92L5xGjUVQ1L6nDriz8mXS7qfU3v/nF5++eVud04d8ef0yiuvdLtzau+f0//+97+UPycDBPWkZ1V1u6qWAK8CU5pZ9zOeeccleyDRZB+naiMR6Qm8Alytqo+KyBCghKD1+ZfAUFU9v8k2pwPHqOpXw8/nAPuq6ndFZIuq9k1Yd7Oq9hORPwNvquo94fzbgVnAKuAaVT0qnH8wcLGqntjkmFcAc8NynUtQof2xNnnJdVFRkc6ePbsjoulytbW1ZGfb0LBNWS47qCpbtz/M+i23gayjMHdfBve9jLycsQBU121g/trDiev2hm2EHPJzxjFxl6e7xS33lti14rJM/CwXV6pnIiLFHdCK2CaF44bpuOsvaPd+3jvxV0mdQ9g4+C9VneRZtidwE8HA87nAO8AZqrogYZ1vAt8CdgOWJ2zeC3hDVc9Opryd2hIaDlj6CHCvqj4KoKrrVbUurNzdRnDrvanVwMiEzyOA+sEH14vI0HD/Q4ENrWyzOvzdt6/6cg4D9lHVJ4DLgS8CVQRN0d3GsmXLoi5CSrJcdigpvYF1Wy5jw9pdqYtvYlvlc6xY/1mqaz8CIDdrMBOGPEBBzl4I2Qg59Ck4nAmD7+32FVCwa8XHMvGzXFyWScu66o1JInIf8Cawh4isFpELRORCEbkwKIe+DzwLvEdQAf1bYgU09E+Cvp9P0rgv6IxkK6DQiX1Cw4d8bgfeV9XrEuYPTehLcCrgG6fqf8A4ERkDrCF44OhL4bInCW6b/yb8+UTC/H+GT7YPI3gA6R1VrRORbSIyk6A7wLnAn5oc75cEDyRB0P9BgThBX9FuY8SIEa2vlIEsl0A8vp2SbTehWkmvfv8L5ypxraBk640MG/AHAHrkTmLS0FnUxcsQyXaGY+rO7FpxWSZ+lovLMkkNqnpmEuv8DvhdC8u3AluBMwFEZDCQD/QUkZ71T9a3pjNbQg8keNDniCbDMf22/pF+4HDghxC0RorILABVrQW+AzwHvA88qKoLw/3+BviMiCwl6Ivwm3CbhcCDBA8vPQt8W1XrH+H9JvA3YBlBs3H9Q0mIyLRw+3fDWbcD84Hp4X66jZKSkqiLkJIsl0BV7Qok/F5aUTY+YUkd5dX/c9bPivXMqAoo2LXiY5n4WS4uy6R5wWDz6ffueBE5MayPrSDoermShDpWazrz6fjXwTtolTMkU7j+WuD4hM+zfOuq6kaauU2uqlcDV3vmzwacfg/hsneBCxI+3wDc4Fs33fXs2TPqIqQkyyWQk7ULqjUA5Oatb7QsN3t0BCVKPXatuCwTP8vFZZm0LKLXbrbXrwiG4XxBVaeJyOGEraPJsDcmZZCampqoi5CSMimXeLyMLdtuY92nX2TDph9SVT2/YVl21kB6FnwGIY+6uoKG+SIFDOz93SiKm3Iy6VpJlmXiZ7m4LJNuqSZsHIyJSExVXwKmJrtx6j6mZjpcPB5vfaUMlCm5xOOlrF5/NHV1G1AqgBjbK55gYN8/0KvHqQAM738D6zb/lG2bPkLIIyvWhyH9fkVh3j7RFj5FZMq1sjMsEz/LxWWZtKyTByvqLFvCUZBeBe4VkQ0ELytKilVCM0hhYbd6zqrDZEouW7bdSl3dJyj1r/WNo1rBxi2X0LPws4jkEosVMHzA9eTzCX36xsiKDUTEbpjUy5RrZWdYJn6Wi8syaVkUfTo7wMlAJcHzPWcBfYBfJLux/d8lg2za1NKrYjNXpuRSXvlMQgV0B0Wprnm/0bwtW7aTnTXYKqBNZMq1sjMsEz/LxWWZNE9p/0NJUVRiwwHt61S1VlXvAv4NXJvs9vZ/mAwybNiwqIuQkjIll1jM93IygDpisd6N5mRKJjvLcnFZJn6Wi8sy6T5EZG8R+beILBCRX4nIEBF5BHiBYJSipFglNIOsWLEi6iKkpO6Ui2qc6upiqipfIR7f3mhZn54XINL0dlgWOdm7k5M9ptHc7pRJR7JcXJaJn+Xiskxaph0wdaHbCAas/zzwKTAH+BAYq6rXJ7sT6xOaQSZMmBB1EVJSd8mlpuYDNpecRTCGcAyllt59fkVhj2C0jML8Y+nT8+ts3fZXkFygjuysYewy4E5nX90lk45mubgsEz/LxWWZtEDTrk9onqreGf7+gYhcBFyaMD57UqwlNIPMnTs36iKkpO6Qi2odm0u+SDy+FtXtqG4DraB06/9RUx28lExE6N/nEkYNnc2Q/n9h2KBHGDHkVbKzhzv76w6ZdAbLxWWZ+FkuLsukW8kXkWkiMl1EpgNlwN4Jn5MimqZjAkSlqKhIZ8+eHXUxjGmkqup1tmw8H9WyJkuyKCj8En36Jd1P3BhjMpKIFKtqURTHzt99uI669sJ272fp6T/vknMQkZdaWKyqekQy+7GW0AxSXFwcdRFSUnfIReNbm1lSRzy+86/K6w6ZdAbLxWWZ+FkuLsukZen0dLyqHt7ClFQFFJLoEyoi44GfALsmrr8zBzGpYcaMGVEXISV1h1xycvdreOVmI1JIfsFxO72/7pBJZ7BcXJaJn+XiskxMU8m0hD5E8NTT5QSV0frJpJk5c+ZEXYSUlC65xOtKqCj9A2UlZ1K+5Qrqalc2LMvKGkjPXt8H2fG6TaSA7Ozx5BectNPHSpdMuprl4rJM/CwXl2XSMtX2T+mm1T6hYR8J+/oSSuc+ofF4nFjMemA0lQ651NWuouzTE1AtB6qAbJBceg64l+zcHd1/qqpep7zsLlRLycs/kcIepyOSt9PHS4dMomC5uCwTP8vFleqZRNknNG/34Tri199q934+POPyyM6hLZK5Gp4SkW+JyFAR6V8/dXrJTIdbvHhx1EVISemQS2XpNeHQS/VvPKoFLad8S+ObEnl5B9FvwG30H/gAPXqe3aYKKKRHJlGwXFyWiZ/l4rJMuicRGS4iB4jIIfVTstsmM07ol8Ofif+3U2C3nSmkid6YMWNaXykDpUMuNVWvAnFnfrx2JRovRZq88ai90iGTKFguLsvEz3JxWSYtUCC9xgkFQESuBb5I8Jak+jFCFXg1me1bbAmV4MXRl6rqmCaTVUDT0Nq1a6MuQkpKh1xEejSzJBYOPN+x0iGTKFguLsvEz3JxWSYtS9M+oacAe6jq8ap6Yjgl/SBCi5VQVY0D325nAU2K6N/felH4pEMueT2+3PihIwByyck/BpH8Dj9eOmQSBcvFZZn4WS4uy6QVafbeztCHQE5bN06mT+jzInKRiIy0PqHprby8POoipKRUyEXj5VSV/o7y9ftTvn4mVaXXognvfs/r+Q1y8o8F8kB6AQVk5U6jsG/nDEKfCpmkIsvFZZn4WS4uy6RbKgfmisgtInJj/ZTsxsn0CT0//JnYImp9QtNQKj+VGKWoc1GNU7npTOI171P/4FHt9tuJV71K/sAnEclCJJse/W4k3uti6moXE8saRVbO+E4rU9SZpCrLxWWZ+FkuLsukJV072HwHejKc2qTVSqiqWk/ibiInp80t5t1a1LnUVb9OvHYpO558B6giXreCuqpXyM7f8V6IWPYIYtkjOr1MUWeSqiwXl2XiZ7m4LJNWpNk4nyKSBZyjqke1dR+tfi0RkXN9U1sPaKJTVtb0veIGos8lXvMeaIW7QLcHyyIQdSapynJxWSZ+lovLMuleVLUOKBeRPm3dRzK34/dJ+D0fOJLgDUp3t/WgJhoDBw6MuggpKepcYlnDg4eOdHuTJYVI1vBIyhR1JqnKcnFZJn6Wi8syaYGSrrfjK4H5IvI80PA/MVX9XjIbt9oSqqrfTZi+BkwDOn5MGNPpVq9eHXURUlLUuWTlHweSByT+AyQguWTnfzaSMkWdSaqyXFyWiZ/l4rJMWpGeT8c/DfyMYFzQ4oQpKcm0hDZVDoxrw3YmYmPHjo26CCmpK3KJVxdTu+0mtO5DYjmTyOr5PWI5ewAgkk/BgEeo2vL98OEkQbLHk9/vj0issNPL5mPXip/l4rJM/CwXl2XS/ajqXe3ZPpk+oU+JyJPh9C/gA9rxJJSJzsKFC6MuQkrq7FzqKv9DzcZz0OpXoO5j4pXPUbPxc8Rr5jesE8vejYKBT1E45B0KB79F4aCniWVH9w+2XSt+lovLMvGzXFyWSWukA6YuLrHIChH5sOmU7PbJtIT+PuH3WuAjVbU29TQ0ZcqUqIuQkjozF1WltvRKgm4z9eKgFdSWXkPugH82Wl9iqTEEr10rfpaLyzLxs1xclkkr0uzp+FBRwu/5wOlA0v8jS2bQruNV9ZVwekNVV4fvCjVpprg46W4aGaVTc9FyqFvnXxTRk+/JsGvFz3JxWSZ+lovLMmlFGvYJVdWNCdMaVb0BOKK17eolUwn9jGfecckewKSOGTNmRF2ElNSpuUhe8+92T5FWTx+7VvwsF5dl4me5uCyT7kdEpidMRSJyIdAr2e2brYSKyDdFZD6wh4i8lzCtAFK3Ccc0y76F+nVmLiLZxAq+RHCXIlEBWT0u7LTjtpddK36Wi8sy8bNcXJZJCxRQaf/U9f6QMF0DTAe+kOzGoupvvw0HH+0X7vTShEXbVHVTW0ub7oqKinT27NlRF8OkGK1dBlWvghRC/tGN+naq1lC79QriFY+C5IDWkdXja2T1+gEiaTkunDHGdDsiUqyqRa2v2fHyRo/QXX6e1NCaLVp1wSVdeg4ispuqfthk3hhVXZHM9s22hKrqVlVdqapnAiOBI1T1IyAmIvYqzzQ0f/781lfKQO3JRVWJl16DlnwO3fYHtPQadMNhaOVLDeuI5JDT99fkDnmHnAGPkjtkNtm9f5jSFVC7VvwsF5dl4me5uCyTbunhJOd5tfp0vIhcQfD00x7A3wkGqr8HODDZg5jUMH78+KiLkJLalUv1O1B+Pzuefq8BQLf+AHLfbDTOp8R6I7HebT9WF7Jrxc9ycVkmfpaLyzJpRRo9HS8iE4CJQB8R+VzCot64/c+alcyDSacCJxG+jklV17ITnU5N6li1alXURUhJ7clFKx+n8fBL9WJQ/Uab9xs1u1b8LBeXZeJnubgsk1akV5/QPYATgL7AiQnTdOBrye4kmXFCq1VVRUQBRKTHThfVpIQhQ4ZEXYSU1K5cNE7zX1/jbd9vxOxa8bNcXJaJn+Xisky6D1V9AnhCRPZX1Tfbup9kWkIfFJFbgL4i8jXgReBvbT2gic6WLVuiLkJKajGXrVth4sTgp4cUnAhS4C7QOshN3x4rdq34WS4uy8TPcnFZJi0Tbf8UgY0i8qKILAAQkb1F5PJkN261EqqqvyfoZPoIQfPrz1T1xraW1kQnPz/pbhoZpcVc/vUvWLQInn7avzz3QMj/LEEXmBhBl+k86HMNEuvZ8YXtInat+FkuLsvEz3JxWSYt6IiB6qOphN4GXEb4QISqvgeckezGLd6OF5EsoJ+qPg88LyK5wHki8r6q7tn2MhuTJu66CwC97WL06Acg72Ck8CtI1gAARATp82u08Ay08qWg4pl/PJI1NMpSG2OMSSuRjfPZXoWq+k6T0V5qk9242UqoiJwB3AJsF5GlwJXAP4D/AWe1qagmUpWVvgdoTLO5lJWhr76MALy5FkrzoXA5WvEIDHgKyRrYsKrk7I3k7N0l5e0Kdq34WS4uy8TPcnFZJt1SiYjsTtgOKyKnAf53VXu01BJ6OTBDVZeJyHTgTeAMVX2sPaU10enbt2/URUhJzeWis56CnDqoAnKA/2yHE2IQL0W3/w3pfal3u+7ArhU/y8VlmfhZLi7LpBVpNERTgm8DtwITRGQNsIKdaKhsqRJararLAFR1joissApoelu/fj29e6fHOJVdaf2779L7v/91Fzz4D6QseMJdyhT+uBn9MBgHlNgd0CP8F+PYY2HatC4qbdewa8XPcnFZJn6Wi8syaUUaVkLDtyUdFY6cFAMqgC8CHyWzfUuV0MEi8qOEzz0TP6vqdW0or4nQqFGjoi5CShpVVQW/+AVUV0NLbzFaVIUsqgo/bAT9P8jNhaJI3vLWqexa8bNcXJaJn+Xisky6DxHpTdAKOhx4Angh/HwRMA+4N5n9tPR0/G0Eg9LXT00/mzSzZMmSqIuQkpYMHQrz58Mee0B+PsTjEI8j8cbjfEp8x0R+HkyYAAsWwGc+E03BO5FdK36Wi8sy8bNcXJZJK9Lr6fh/EIyYNJ9gcPp/A6cDp6jqycnuRFTTsP03QkVFRTp79uyoi2E6Q3U1XHQR3H47lJc3u5oW5CBf+yb87ndBS6gxxpi0JyLFqhrJra28USN16CU/aPd+PvrORV1yDiIyX1Unh79nASXAKFXdtjP7SWawetNNFBcXR12ElKNaw+x3niK+9WfEq25Br78MHngAYv6/GhqLIQ89Bn/8Y7eugNq14me5uCwTP8vFZZl0KzX1v6hqHbBiZyugkNxrO003MWPGjKiLkFJUK9GNX2L6rh9CRTmQg26/DTgP6dEDtrl/n6RHD8jJ6fKydjW7VvwsF5dl4me5uCyTlkX0xqO2miIipeHvAhSEnwVQVU3qCTRrCc0g9i20Md1+L9QuY87S+u4rNUAl3PlrtKxsx4o9E958VFYG99zTlcWMhF0rfpaLyzLxs1xclkkr0qhPqKpmqWrvcOqlqtkJvyc9BEKzfUKbPBnvK0BGPh1vfUK7j3jJKVC7qPHMWkUmfIhsjwcPKQ0YANdeCxdfDJs2QWUl9OoFmzdDVlYk5TbGGNPxou4TOuwnP2j3flZ+r2v6hHaUllpC65+CLwK+SfAY/nDgQmCvzi+a6Wjz5s2LugipRfIAeG/F53bMe7sCquJoYQGcfDIsXgxnnRX8POkkKCyEigp47bWICt017Frxs1xclomf5eKyTExTzVZCVfUXqvoLYCAwXVV/rKo/BmYAI7qqgKbjTJw4MeoipBQpPBOkgL1G/WvHvKfKICsGt9wK99+/41Z8r17BA0u33BK0gD74YESl7hp2rfhZLi7LxM9ycVkmLRNt/5RukukTOgqoTvhcDYzulNKYTrVs2bKoi5Ba8k+C/GNZvu4IoACkB/rFEfDe88jZZ/u3OftsWLQIzjuvK0va5exa8bNcXJaJn+XiskxaodL+Kc0k83T8P4B3ROQxgm6vpwJ3d2qpTKcYMcIasBOJxJA+1zJi3CIk50CIDYRjD0aklaGXdtstmLoxu1b8LBeXZeJnubgsk+5DRLbRwqNQyT6c1GolVFWvFpFngIPDWV9R1XeTKqVJKSUlJfRMfNK7m1NVtPJJ4uX/gHgZkn8ssR4XILHGL/zauLmQXqO/GFEpU1OmXSvJslxclomf5eKyTFrQ9W88ahdV7QUgIlcBnxA0WApwFjvxVs1kh2gqBEpV9Y/AahEZs3PFNakg0/7yx0uvIr71cqiZC3XL0O23UrfxVFQrGq2XabkkwzLxs1xclomf5eKyTFrRRUM0icgdIrJBRBY0s/wwEdkqInPD6ect7O4YVf2Lqm5T1VJV/Svw+eRKkkQlVESuAC4BLgtn5QDdf6DEbqimpqb1lboJrVuLVjwIJFY4q6FuPfHyxxqtm0m5JMsy8bNcXJaJn+XiskxSxp3Asa2s85qqTg2nq1pYr05EzhKRLBGJichZQF2yBUmmJfRU4CRgO4CqrmUnmlpN6ojH41EXoctozVwQ35uNKqD69UZzMimXZFkmfpaLyzLxs1xclknLuurpeFV9FdjUQcX+EvAFYH04nR7OS0oyDyZVq6qKBKcnIj3aUkoTvcLCwqiL0GUkNgj/vYlsyBrWaE4m5ZIsy8TPcnFZJn6Wi8syaUXH9AkdKCKJb9S5VVVvbcN+9heRecBa4CJVXehbSVVXAif7liUjmZbQB0XkFqCviHwNeAH4W1sPaKKzaVNHffFJAzkzINYf9xLPIVbY+EtaRuWSJMvEz3JxWSZ+lovLMukSJapalDC1pQI6B9hVVacAfwIeb25FERkvIi/W9y8Vkb1F5PJkD9RqJVRVfw88DDwC7AH8XFVvTPYAJnUMGzas9ZW6CZEYWf3vhew9gTyQQpB+xPr+EcluPLxSJuWSLMvEz3JxWSZ+lovLMmlFirw7PnzAqCz8fRaQIyIDm1n9NoJnhmrC9d8Dzkj2WMk8mHStqj6vqj9R1YtU9XkRuTbZA5jUsWLFiqiL0KUkaxjZA58ga+CzZPV/iKzBbxHLP8JZL9NySYZl4me5uCwTP8vFZZk0ryP6g3bUG5NEZBcRkfD3fQnqihubWb1QVd9pMq822WMl0yf0MwRPxyc6zjPPpLgJEyZEXYQOp1qF1n2CxAYhMX9/I8ke2eI+umMu7WWZ+FkuLsvEz3JxWSat6KI3HonIfcBhBP1HVwNXEIx8hKreDJwGfFNEagmGmDlDVZur4paIyO6E7bAichqwLtmyNNsSKiLfFJH5wAQReS9hWgHMT/YAJnXMnTs36iJ0GFWlquxmtn0ylbJPj2HbJ1Op2HI5qkl/AWvQnXLpKJaJn+Xiskz8LBeXZZIaVPVMVR2qqjmqOkJVb1fVm8MKKKp6k6pOVNUpqjpTVf/bwu6+DdxCUFdcA/wAuDDZskhzlVsR6QP0A64BLk1YtE1VM7Z3cVFRkc6ePbv1FU2nqt7+CJVbf0qjcUClgNzCc8jvk3SfaGOMMQYAESlW1aIojp0/YqSO+O6P2r2f5Zf+qEvPQUTGqOqKcOSkmKpuq5+XzPbNtoSq6tbw0fs/AptU9SNV/QioEZH9OqT0pksVFxdHXYQOU132JxoPRA9oBdXl/0B15wZE7k65dBTLxM9ycVkmfpaLyzJpWar0Cd1JjwCo6nZV3RbOezjZjZPpE/pXYHrC5+2eeSYNzJgxI+oidJh4fIN/gdaCloP0SXpf3SmXjmKZ+FkuLsvEz3JxWSbdh4hMACYCfUTkcwmLegP5ye4nmXFCJbFDqqrGSa7yalLMnDlzoi5Ch8nKmeydL7H+IL13al/dKZeOYpn4WS4uy8TPcnFZJq1IkSGakrQHcALQFzgxYZoOfC3ZnSRTmfxQRL5H0PoJ8C3gw50pqUkNU6dOjboIHSa/90/ZvvELoJXs+JuXT17vKwhHlkhad8qlo1gmfpaLyzLxs1xclkkLorud3iaq+gTwhIjsr6pvtnU/ybSEXggcAKwBVgP7AV9v6wFNdBYvXhx1ETpMVu4Uegx8lOy8I5HYELJy96VwwB3kFp6w0/vqTrl0FMvEz3JxWSZ+lovLMumWThWR3iKSE745qUREzk5241ZbQlV1Azsx+r1JXWPGjIm6CB0qK2cihQPuaPd+ulsuHcEy8bNcXJaJn+XiskxakUYtoQmOVtWLReRUgobK04GXgHuS2TiZNya1672gJnWsXbs26iKkJMvFZZn4WS4uy8TPcnFZJq1Irz6h9XLCn8cD9+3sEJ7J3I5v13tBTero379/1EXYKfF4KdvLn2J7+ZPE46Wddpx0y6UrWCZ+lovLMvGzXFyWScvSdIimp0RkMVAEvCgig4DKZDdOphLarveCmtRRXl4edRGStr38aT5eN5WSzT+iZPOP+XjdFMrKn+yUY6VTLl3FMvGzXFyWiZ/l4rJMuh9VvRTYHyjSYJDu7cDJyW6fTCW0Te8FFZGRIvKSiLwvIgtF5Pvh/P4i8ryILA1/9mtm+2NF5AMRWSYilybMb3Z7EbksXP8DETkmYf4MEZkfLrtRwsenReS7IrJARGaJSG447yARuS6JXNJOLJbMH3f0aus2ULLpO6hWoFoWTpVs3Px9aus+6fDjpUsuXcky8bNcXJaJn+Xisky6HxE5l2BoprPC308Djk52+2SuiLa+F7QW+LGq7gnMBL4tInsRvAL0RVUdB7xI41eCAiAiWcCfgeOAvYAzw21pbvtw+RkEg6ceC/wl3A8Ew0t9HRgXTseG878K7A28CxwTVk5/BvwyifNLOzk5Oa2vlALKK57yzldVtndCa2i65NKVLBM/y8VlmfhZLi7LpBXp2Sd0n4TpYOBK4KRkN261EqqqH6rqUcAgYIKqHhS+vrO17dap6pzw923A+8Bwgmbau8LV7gJO8Wy+L7AsPHY1cD87mneb2/5k4H5VrQrfWboM2FdEhgK9VfXNcND9u5scMwcoJOjzeg4wS1U3t3Z+6aisrCzqIiRF4xWot8dHLaoVnvntky65dCXLxM9ycVkmfpaLyzLpflT1uwnT14BpQG6y2yfzdPwAEbkReA14WUT+KCIDdqaQIjI6LNjbwBBVXRcWfh0w2LPJcODjhM+rw3m0sH1z2wwPf/ft6/fAWwQV7DeALwN/aelc1q1bx9SpU5kyZQpTpkzh+uuvZ/ny5VRUVLBo0SLi8XjDWyHq35M7Z84c4vE4ixYtoqKiguXLl7N582bWrFnDunXrKCkpYeXKlZSVlbF48WJqa2uZN29eo33U/5w/fz5VVVUsXbqU0tJSVq1axYYNG9iwYQOrVq2itLSUpUuXUlVVxfz58xttu379egDmzZtHbW0tixcvpqysjJUrV1JSUsK6detYs2YNmzdvjvScNm7ch22lk9hWuiebNs6komIYGz45mrrafny0Yop3H+05p/pcUuXPqSPOqb1/TgMHDux259QRf06+XNL9nNr757Rly5Zud04d8edUWlra7c6pvX9O+fn5KX9OkemAh5JSZLD7coI7zkmRhDdy+lcQeR54lR1jPp0FHBa2jrZ+AJGewCvA1ar6qIhsUdW+Ccs3q2q/JtucDhyjql8NP58D7Kuq321uexH5M/Cmqt4Tzr8dmAWsAq6pL6+IHAxcrKonNjnmFcBcggbtcwkqtD8OX1PaoKioSGfPnp3MqaecxYsXM2HChKiLkZRNW37Otu33oBo8ZCdSQM/CMxjQ7+oOP1Y65dJVLBM/y8VlmfhZLq5Uz0REilW1KIpj5w8bqaO//qN27+eDX/yoS89BRJ5iR0eAGEEXygfDB5ZalcxrO/uramIfyV+JyClJFi4HeAS4V1UfDWevF5GhqrouvFW+wbPpamBkwucRQP0AY81t39w2q8PfffuqL+cwYB9V/YWIvEPwpNfVwJHA88mcazoYO3Zs1EVIWr8+v6Cw4DjKyh8FlJ6FnyMvd/9OOVY65dJVLBM/y8VlmfhZLi7LpFv6fcLvtcBHqrq6uZWbSubBpJdE5AwRiYXTF4CnW9sofMjnduB9VU182vxJgtvehD+f8Gz+P2CciIwJn1o/I9yupe2fBM4QkTwRGUPQHPxOeMt+m4jMDMt0rueYvyR4IAmggKBWHyfoK9ptLFy4MOoiJE1EyM/bn4H9fsfAfr8nP++AnX4nfLLSKZeuYpn4WS4uy8TPcnFZJq1IwweTVPWVhOmNnamAQnK347cBPYC6cFYWwThQ4fG1dzPbHUTQj3Q+QYUO4KcE/UIfBEYR3Co/XVU3ha2Rf1PV48PtjwduCI93h6peHc4f4Ns+XPZ/wPkEtfEfqOoz4fwi4E6CCuYzwHfDh5QQkWnAd1T1gvDzD4CvEdyOP1lVqxLPK51vxxtjjDHGL8rb8QXDRuror7b/dvziX3bN7XgReV1VDwrriL6K5Ebgd6ra4nM2rVZCTWPpXAktLi5mxowZURcj5VguLsvEz3JxWSZ+losr1TOJvBJ6QQdUQn/VtX1CmxM2GP5XVfdoab1kno6/oMnnrPAhHpNmUukvv2odmyteYV3p3ZRWvkOUX4ZSKZdUYZn4WS4uy8TPcnFZJt1H+OKgZidV3Qgc1tp+kukTemT4RqGhIjKZYEijXu0sv4lA/fAVUauu+5R31x7FB59+h5Wbr2HRhvOZ/8nnqYtH80q3VMkllVgmfpaLyzLxs1xclkkL0m+IpmJgdvjzU2AJsDT8vRgahtFsUatPx6vql0TkiwR9O8uBM1X1jbaX20QlVb6FLtt4KZW1ayAckF4VtlcvYtWW6xjT//IuL0+q5JJKLBM/y8VlmfhZLi7LpBVp1DtSVccAiMjNwJOqOiv8fByQ1BCekNzt+HHA9wmGWloJnCMi3eqp8UxRPzhwlOJaxdaK16DJG5GUaj7d/lgkZUqFXFKNZeJnubgsEz/LxWWZdEv71FdAAcIHwg9NduNkxgl9Cvi2qr4YDnH0I4IhlCbubElNtMaPHx91EVBVtJmve6q+V3V2vlTIJdVYJn6Wi8sy8bNcXJZJK9KoJTRBiYhcTvBCIwXOJngyPinJ9AndV1VfhGA8JlX9A/73vZsUt2rVqqiLQFYsn155U4GmY35m07/w6AhKlBq5pBrLxM9ycVkmfpaLyzJpWZr1Ca13JsGrzx8DHg9/PyPZjZuthIrIxQCqWhq+RjPRV3a6mCZyQ4YMiboIAIwdcC3ZsT7EpACAmBSSlzWEXftdEkl5UiWXVGKZ+FkuLsvEz3JxWSbdj6puUtXvq+o0VZ0GXAd8NdntW2oJTazJXtZk2bE7UUaTIrZs2RJ1EQAoyNmN6cNfZte+l7FLz3PZrf9VTBv+ArlZAyMpT6rkkkosEz/LxWWZ+FkuLsukFWn4xiQAERkoIt8UkVeBl4Ckv2201CdUmvnd99mkgfz8/KiL0CA71puhvc+OuhhAauWSKiwTP8vFZZn4WS4uy6QFEVYi20JEegGnAl8CxhPcjt9NVUfszH5aqoRqM7/7PhtjjDHGmDaKqE9nW20A3gEuB15XVRWRU3d2Jy1VQqeISClBq2dB+DvhZ/s6k4YqKyujLkJKslxclomf5eKyTPwsF5dl0q38lKDb5l+Bf4rIA23ZSbN9QlU1S1V7q2ovVc0Of6//nNPGQpsI9e3bN+oipCTLxWWZ+FkuLsvEz3JxWSatSKM+oap6varuB5xE0Dj5ODBMRC4RkaTH4kpmiCbTTaxfvz7qIqQky8VlmfhZLi7LxM9ycVkmLUvHIZpU9UNVvVpVJwP7AH2AZ5LdPpnB6k03MWrUqC45zraatfzv0z+ytvwdsiSXsb1PYPqAr5MVy+uS4++srsolnVgmfpaLyzLxs1xclkn3pqrzCV7x/tNkt7GW0AyyZMmSTj9GVV0pT6+6gNXb36BOq6iOb+ODrY/w4rqLO/3YbdUVuaQby8TPcnFZJn6Wi8syaUUa3Y7vKFYJzSCTJ0/u9GMs3foktVqJEm+YV6fVbKiYz+aq5Z1+/LboilzSjWXiZ7m4LBM/y8VlmbSgIyqgVgk1qay4uLjTj1FS+T51WuXMF2IpWwntilzSjWXiZ7m4LBM/y8VlmXQ/IjLDM+/EZLe3SmgGmTHDuVY6XL+83cmSXM+SOL1zR3b68duiK3JJN5aJn+Xiskz8LBeXZdI86aApAreJSEMTt4icSTB2aFKsEppBuuJb6Pg+pxCTxiN4xcihb+5uDMib0OnHbwv7du6yTPwsF5dl4me5uCyTVqTn7fjTgLtEZE8R+RrwLeDoZDcW1TTsRBChoqIinT17dtTFSGmbq5bz5obf8mnlAmJkM7rXkew36MfkZvWIumjGGGOMl4gUq2pRFMcuHDJSx37pR+3ez/wbftTl5xCOC/o48DFwiqpWJLuttYRmkHnz5nXJcfrl7c7xI2/hnLGvcPbYlzh4l5+ndAW0q3JJJ5aJn+Xiskz8LBeXZdKydBonVETmi8h7IvIe8DDQHxgNvB3OS4qNE5pBJk6c2KXHi0l6XF5dnUs6sEz8LBeXZeJnubgsk1ak143pEzpiJ9YSmkGWLVsWdRFSkuXiskz8LBeXZeJnubgsk1akUZ9QVf1IVT8iaMz8JPx9DHAysDXZ/VglNIOMGDEi6iKkJMvFZZn4WS4uy8TPcnFZJt3SI0CdiIwFbieoiP4z2Y2tEppBSkpKoi5CSrJcXJaJn+Xiskz8LBeXZdKCDugPGsW744G4qtYCnwNuUNUfAkOT3Tg9Ou2ZDtGzZ8+oi5CSLBeXZeJnubgsEz/LxWWZtCK9+oTWqwnHBj0XqB+kPqeF9RuxltAMUlNTE3URUpLl4rJM/CwXl2XiZ7m4LJNu6SvA/sDVqrpCRMYA9yS7sbWEZpB4PN76SklYXb6CVz59ji3VG9mz9xQOGHgE+VmFHbLvKHRULt2JZeJnubgsEz/LxWWZtCyi2+ntoqqLgO8lfF4B/CbZ7a0SmkEKC9tfUXx385vc+9HN1GoNirJi+xJeK3mOn+xxDYXZ6XmrpSNy6W4sEz/LxWWZ+FkuLsukFWlUCRWRB1X1CyIyn8YlF0BVde9k9mO34zPIpk2b2rV9ndbywMd/o0ar0fCaq9FqSmu28NKGpzuiiJFoby7dkWXiZ7m4LBM/y8VlmXQr3w9/nkDQF7R+qv+cFKuEZpBhw4a1a/t1FauJq3s7pVZreW9r+r7KtL25dEeWiZ/l4rJM/CwXl2XSsnR6Ol5V14U/P0qcgNXAQcnuxyqhGWTFihXt2r4gqwdxrfMuK8xKz1vx0P5cuiPLxM9ycVkmfpaLyzJpQUcMVN+1r+3sLSKXichNInK0BL4LfAh8Idn9WJ/QDDJhwoR2bT8gbxDDCkbxcfkK4uxoEc2N5XHY4OPaW7zItDeX7sgy8bNcXJaJn+XiskxakUZ9QoF/AJuBN4GvAj8BcoGTVXVusjuxltAMMnfu3Hbv44LdfsTg/GHkxvLIjxWQLTkcOug49u6zT/sLGJGOyKW7sUz8LBeXZeJnubgsk25lN1U9T1VvAc4EioATdqYCCiCq6VX1jlpRUZHOnp2+/R87gqqypmIlpTVbGVW4Gz1zekddJGOMMaZdRKRYVYuiOHaPQSN1wqk/avd+5tz2oy45BxGZo6rTm/ucLGsJzSDFxcUdsh8RYUThGPbqM7VbVEA7KpfuxDLxs1xclomf5eKyTFrRRX1CReQOEdkgIgtaWW8fEakTkdM8i6eISGk4bQP2rv9dREqTK4lVQjPKjBkzoi5CSrJcXJaJn+Xiskz8LBeXZZIy7gSObWkFEckCrgWe8y1X1SxV7R1OvVQ1O+H3pFunrBKaQebMmRN1EVKS5eKyTPwsF5dl4me5uCyTlolqu6dkqOqrQGuDtn4XeATY0M7TapE9HZ9Bpk6dGnURUpLl4rJM/CwXl2XiZ7m4LJMWdNwQSwNFJPHBlVtV9dad2YGIDAdOBY4AOvWpY2sJzSCLFy+OuggpyXJxWSZ+lovLMvGzXFyWSZcoUdWihGmnKqChG4BLVJsZGLwDWUtoBhkzZkzURUhJlovLMvGzXFyWiZ/l4rJMWtaVbzxqRRFwv4gADASOF5FaVX28ow9kLaEZZO3atVEXISVZLi7LxM9ycVkmfpaLyzJpRYq8MUlVx6jqaFUdDTwMfKszKqBgLaEZpX///lEXISVZLi7LxM9ycVkmfpaLyzJpWVe1hIrIfcBhBP1HVwNXADkAqnpz15QiYJXQDFJeXk6/fv2iLkbKsVxclomf5eKyTPwsF5dlkhpU9cydWPe8TiyKVUIzSSzWeu+Lj7eXcPOy53h304f0ze3B2aMP5bhh0wn7hnRLyeSSaSwTP8vFZZn4WS4uy6QVqdMntMtYJTSD5OTktLh8XcUmvvL2TZTXVqEom2u287v3H2d1+Ua+Pu7oLipl12stl0xkmfhZLi7LxM9ycVkmLdCUejCpy9jXkgxSVlbW4vK7V7xMZW01mvB1rDJewz8/epXttZWdXbzItJZLJrJM/CwXl2XiZ7m4LBPTlFVCM8jAgQNbXP7e5o+oI+7Mz5YsPtr+aWcVK3Kt5ZKJLBM/y8VlmfhZLi7LpBUp8nR8V7JKaAZZvXp1i8uHFw7A1/OzRusYnN+ncwqVAlrLJRNZJn6Wi8sy8bNcXJZJ84Tgdnx7p3RjldAMMnbs2BaXnzvmMHJjjfvs5MaymTlgPAPzendiyaLVWi6ZyDLxs1xclomf5eKyTExTVgnNIAsXLmxx+aS+o7hy8hcZkNuL3Fg2ubFsDh8yiV/sfUYXlTAareWSiSwTP8vFZZn4WS4uy6QVqu2f0oxoGhY6SkVFRTp79uyoi9Gp4hpnY1UZPbPzKcjOjbo4xhhjTKcTkWJVLYri2D0HjNTJx/yg3ft5676LIjuHtrCW0AxSXFyc1HoxiTEov3fGVECTzSWTWCZ+lovLMvGzXFyWSQs64qGkNGxTtEpoBpkxY0bURUhJlovLMvGzXFyWiZ/l4rJMTFNWCc0g9i3Uz3JxWSZ+lovLMvGzXFyWScsk3v4p3dgbkzKIfQv1s1xclomf5eKyTPwsF5dl0oo0vJ3eXtYSmkHmz58fdRFSkuXiskz8LBeXZeJnubgsE9OUtYRmkPHjx0ddhJRkubgsEz/LxWWZ+FkuLsukZek42Hx7WUtoBlm1alXURUhJlovLMvGzXFyWiZ/l4rJMWqBk5DihVgnNIEOGDIm6CCnJcnFZJn6Wi8sy8bNcXJaJacoqoRlky5YtURchJVkuLsvEz3JxWSZ+lovLMmlZJr473vqEZpD8/Pyoi5CSLBeXZeJnubgsEz/LxWWZtCINK5HtZS2hxhhjjDGmy1lLaAaprKyMuggpyXJxWSZ+lovLMvGzXFyWSfOE9Lyd3l5WCc0gffv2jboIKclycVkmfpaLyzLxs1xclkkL0vTp9vay2/EZZP369azdVsotc/7HH956nTnr1qIZeNE3tX79+qiLkHIsEz/LxWWZ+FkuLsukZfZgkunWPqit5if3/J24KjXxOm6fO4djdx/HH446FhGJuniRGTVqVNRFSDmWiZ/l4rJM/CwXl2VimrKW0AxRVl3NK3OKqayrpTpehwIVtTU8t3wpr6xaGXXxIrVkyZKoi5ByLBM/y8VlmfhZLi7LpBXaAVOasUpohvjv6o94dvtWZ355bQ2PLV4UQYlSx+TJk6MuQsqxTPwsF5dl4me5uCyTlmXi7XirhGaImMQ4rfcA77KsWGZfBsXFxVEXIeVYJn6Wi8sy8bNcXJaJaarTah8icoeIbBCRBQnzrhSRNSIyN5yOb2bbY0XkAxFZJiKXJszvLyLPi8jS8Ge/hGWXhet/ICLHJMyfISLzw2U3Stj5UUS+KyILRGSWiOSG8w4Skes6I4+oHThiFA9tLXHmF2TncNqeEyMoUeqYMWNG1EVIOZaJn+Xiskz8LBeXZdICBeLa/inNdGYT2J3AsZ7516vq1HCa1XShiGQBfwaOA/YCzhSRvcLFlwIvquo44MXwM+HyM4CJ4TH/Eu4H4K/A14Fx4VRfpq8CewPvAseEldOfAb9sz0mnqoKcHH675xQKsrMpyM4mN5ZFflY2Z0yczP7DR0ZdvEjZt3OXZeJnubgsEz/LxWWZtCID+4R22tPxqvqqiIxuw6b7AstU9UMAEbkfOBlYFP48LFzvLuBl4JJw/v2qWgWsEJFlwL4ishLorapvhvu6GzgFeCbcRw5QCNQA5wCzVHVzG8qcFo4/5FAO2Hdfnl2+lO01NRw6ajRj+/tv0WcS+3buskz8LBeXZeJnubgsE9NUFJ0BvyMi74W36/t5lg8HPk74vDqcBzBEVdcBhD8Ht7LN8PB3375+D7wFDALeAL4M/KWtJ5UO5s2bR9/8As6YuDcXTJ1hFdDQvHnzoi5CyrFM/CwXl2XiZ7m4LJOW2YNJne+vwO7AVGAd8AfPOr4BK1uLtrltmt2Xqv5DVaep6tnAj4AbgeNE5GERuV5EvNmsW7eOqVOnMmXKFKZMmcL111/P8uXLqaioYNGiRcTjcebMmQPsuPUwZ84c4vE4ixYtoqKiguXLl7N582bWrFnDunXrKCkpYeXKlZSVlbF48WJqa2sb/rLW76P+5/z586mqqmLp0qWUlpayatUqNmzYwIYNG1i1ahWlpaUsXbqUqqoq5s+f32jb6upqIPiHoLa2lsWLF1NWVsbKlSspKSlh3bp1rFmzhs2bN6fNOdX/bM851dTUdLtzau+f08SJE7vdOXXEn5Mvl3Q/p/b+OWVlZXW7c+qIP6ecnJxud07t/XMaOXJkyp9TpOrfmtSeKc1IZ74xJ7wd/y9VnZTsMhHZH7hSVY8JP18GoKrXiMgHwGGquk5EhgIvq+oeieuE2zwHXAmsBF5S1Qnh/DPD7b+RcLxhwK2qeoKIvAPsD1xN0Pf0+ablLioq0tmzZ7cjlegsXryYCRMmRF2MlGO5uCwTP8vFZZn4WS6uVM9ERIpVtSiKY/fqM0KLZn633ft5+d+XRnYObdGlLaFhxbHeqcACz2r/A8aJyJjwqfUzgCfDZU8S3DYn/PlEwvwzRCRPRMYQPID0TnjLfpuIzAwfPDo3YZt6vyR4IAmggPAZNYK+ot3KiBEjoi5CSrJcXJaJn+Xiskz8LBeXZWKa6swhmu4D3gT2EJHVInIB8NtwuKT3gMOBH4brDhORWQCqWgt8B3gOeB94UFUXhrv9DfAZEVkKfCb8TLj8QYKHl54Fvq2qdeE23wT+BiwDlrPjoSREZFq4/bvhrNuB+cD0cD/dSkmJO0STsVx8LBM/y8VlmfhZLi7LpAUd8WR8+t2N79Sn48/0zL69mXXXAscnfJ4FOMM3qepG4Mhm9nE1wW30pvNnA053gHDZu8AFCZ9vAG7wrdsd9OzZM+oipCTLxWWZ+FkuLsvEz3JxWSbNE0DSsE9ne2X2q3IyTP0DOKYxy8VlmfhZLi7LxM9ycVkmpqlOawk1qScej0ddhJRkubgsEz/LxWWZ+FkuLsukFRkYj1VCM0hhYbd71qpDWC4uy8TPcnFZJn6Wi8syaZndjjfd2qZNm6IuQkqyXFyWiZ/l4rJM/CwXl2VimrKW0AwybNiwqIuQkiwXl2XiZ7m4LBM/y8VlmbQgTZ9uby9rCc0gK1asiLoIKclycVkmfpaLyzLxs1xclklLOuBtSWl4O99aQjNIKr+pIkqWi8sy8bNcXJaJn+Xiskxalo7vfm8vawnNIHPnzo26CCnJcnFZJn6Wi8sy8bNcXJaJaapT3x3fHaXzu+ONMcYY4xflu+N79xqu+077Vrv38+Jrl9u7401qKi4ujroIKclycVkmfpaLyzLxs1xclkkLFCTe/indWEvoTrKWUGOMMab7ibQltOdw3W9q+1tCX3jDWkJNipozZ07URUhJlovLMvGzXFyWiZ/l4rJMWmFPx5vubOrUqVEXISVZLi7LxM9ycVkmfpaLyzJpRfrVIdvNWkIzyOLFi6MuQkqyXFyWiZ/l4rJM/CwXl2VimrKW0AwyZsyYqIuQkiwXl2XiZ7m4LBM/y8VlmbTM3h1vurW1a9dGXYSUZLm4LBM/y8VlmfhZLi7LpBXWJ9R0Z/3794+6CCnJcnFZJn6Wi8sy8bNcXJZJCxRIwyGW2staQjNIeXl51EVISZaLyzLxs1xclomf5eKyTExT1hKaQWIx+87hY7m4LBM/y8VlmfhZLi7LpHmCWp9Q073l5OREXYSUZLm4LBM/y8VlmfhZLi7LpBVd1CdURO4QkQ0isqCZ5SeLyHsiMldEZovIQR16ngmsEppBysrKoi5CSrJcXJaJn+Xiskz8LBeXZZIy7gSObWH5i8AUVZ0KnA/8rbMKYrfjM8jAgQOjLkJKslxclomf5eKyTPwsF5dl0oouuh2vqq+KyOgWlid+W+hBJw6jby2hGWT16tVRFyElWS4uy8TPcnFZJn6Wi8syaUH90/HtnWBgeAu9fvp6W4ojIqeKyGLgaYLW0E5hLaEZZOzYsVEXISVZLi7LxM9ycVkmfpaLyzLpEiWqWtTenajqY8BjInII8EvgqHaXzMNaQjPIwoULoy5CSrJcXJaJn+Xiskz8LBeXZdIyUW331NFU9VVgdxHplL4UVgnNIFOmTIm6CCnJcnFZJn6Wi8sy8bNcXJZJK1LkjUkiMlZEJPx9OpALbOyQnTdhldAMUlxcHHURUpLl4rJM/CwXl2XiZ7m4LJPUICL3AW8Ce4jIahG5QEQuFJELw1U+DywQkbnAn4EvqnbOU1PSSfvttoqKinT27NlRF8MYY4wxHUhEijuiP2Vb9CkcqvuPvaDd+3lu/tWRnUNbWEtoBrFvoX6Wi8sy8bNcXJaJn+XiskxaoKTM7fiuZC2hO8laQo0xxpjuJ9KW0IKhuv9u7R8J6blFv7aWUJOa5s+fH3URUpLl4rJM/CwXl2XiZ7m4LBPTlI0TmkHGjx8fdRFSkuXiskz8LBeXZeJnubgsk5Z1xhBLqc5aQjPIqlWroi5CSrJcXJaJn+Xiskz8LBeXZdKKDOwTapXQDDJkyJCoi5CSLBeXZeJnubgsEz/LxWWZmKasEppBtmzZEnURUpLl4rJM/CwXl2XiZ7m4LJMWKBDX9k9pxvqEZpD8/Pyoi5CSLBeXZeJnubgsEz/LxWWZtCQ9b6e3l7WEGmOMMcaYLmctoRmksrIy6iKkJMvFZZn4WS4uy8TPcnFZJq3IwJZQq4RmkL59+0ZdhJRkubgsEz/LxWWZ+FkuLsukFRlYCbXb8d3I2uWfcN3Xb+bre/+Yq77wB5bO+bDR8vXr10dUstRmubgsEz/LxWWZ+FkuLsukBfZgkklnKxd+zPcO+ClV5dXE6+KsXPgx78yawxWP/IR9jpkKwKhRo6ItZIqyXFyWiZ/l4rJM/CwXl2VimrKW0G7i1p/8g8qySuJ1cQBUlaryam781m1o2MS/ZMmSKIuYsiwXl2XiZ7m4LBM/y8VlmbREQePtn9KMVUK7iYX/XeztTvLp6o2Ul5YDMHny5C4uVXqwXFyWiZ/l4rJM/CwXl2XSCntjkklXvfr39M7PyoqRV5gHQHFxcVcWKW1YLi7LxM9ycVkmfpaLyzIxTVkltJs47UcnNlQ26+Xm5/KZLx9Kdk7Q9XfGjBlRFC3lWS4uy8TPcnFZJn6Wi8syaUGGPphkldBu4uRvH8uJ3zya3PwcCnsXkJufw/4nzeBb13+lYR37FupnubgsEz/LxWWZ+FkuLsukFRl4O140DQsdpaKiIp09e3bUxWhW2ZbtrFm6jkEjB9B/l35RF8cYY4xJCyJSrKpFURy7T+4QPWDIGe3ez7Orb4zsHNrCWkK7mZ59e7DHPmO9FdB58+ZFUKLUZ7m4LBM/y8VlmfhZLi7LpBUZ2BJq44RmkIkTJ0ZdhJRkubgsEz/LxWWZ+FkuLsukJelZiWwvawnNIMuWLYu6CCnJcnFZJn6Wi8sy8bNcXJZJCxSIx9s/pRmrhGaQESNGRF2ElGS5uCwTP8vFZZn4WS4uy8Q0ZZXQDFJSUhJ1EVKS5eKyTPwsF5dl4me5uCyTVlifUNOd9ezpH9A+01kuLsvEz3JxWSZ+lovLMmlFGlYi28taQjNITU1N1EVISZaLyzLxs1xclomf5eKyTExT1hKaQeJp2Gm5K1guLsvEz3JxWSZ+lovLMmlJer7xqL2sEppBCgsLoy5CSrJcXJaJn+Xiskz8LBeXZdICBdXMq6Tb7fgMsmnTpqiLkJIsF5dl4me5uCwTP8vFZZmYpqwlNIMMGzYs6iKkJMvFZZn4WS4uy8TPcnFZJq3IwNvx1hKaQVasWBF1EVKS5eKyTPwsF5dl4me5uCyTVtgQTaY7mzBhQtRFSEmWi8sy8bNcXJaJn+XiskxaoJqWbzxqL2sJzSBz586NuggpyXJxWSZ+lovLMvGzXFyWiWlKNA2bb6NUVFSks2fPjroYxhhjjOlAIlKsqkVRHLtP1kDdv8eJ7d7Pc9vujOwc2sJaQjNIcXFx1EVISZaLyzLxs1xclomf5eKyTFqm8Xi7p3RjLaE7yVpCjTHGmO4n6pbQmQWfbfd+/r39bmsJNalpzpw5URchJVkuLsvEz3JxWSZ+lovLMmlJBzwZn4aNitYSupPSuSU0Ho8Ti9n3jqYsF5dl4me5uCwTP8vFleqZRNoSGhugM/OOb/d+/l15j7WEmtS0ePHiqIuQkiwXl2XiZ7m4LBM/y8VlmZimbJzQDDJmzJioi5CSLBeXZeJnubgsEz/LxWWZtMLeHW+6s7Vr10ZdhJRkubgsEz/LxWWZ+FkuLsukeQpoXNs9pRtrCc0g/fv3j7oIKclycVkmfpaLyzLxs1xclkkLVK0l1HRvt956a9RFSEmWi8sy8bNcXJaJn+XiskxMU1YJzSD//Oc/oy5CSrJcXJaJn+Xiskz8LBeXZdIyux1vujURiboIKclycVkmfpaLyzLxs1xclkkrMvB2vI0TupNE5FPgo6jL0UYDgZKoC5GCLBeXZeJnubgsEz/LxZXqmeyqqoOiOLCIPEuQT3uVqOqxHbCfLmGVUGOMMcYY0+WsT6gxxhhjjOlyVgk1xhhjjDFdziqhxhhjjDGmy1klNIWIyB0iskFEFiTM6y8iz4vI0vBnv2a2PVZEPhCRZSJyaTLbi8hl4fofiMgxCfNniMj8cNmNEj7SKCLfFZEFIjJLRHLDeQeJyHWdkUdCeXy5/E5EFovIeyLymIj0bWbbbpmLL5OEZReJiIqIt5N7d80kPIY3l7A8H4jIQhH5bTPbdstcmvn7M1VE3hKRuSIyW0T2bWbb7prJSBF5SUTeD6+J77d2XhmeS0b/e2s6karalCITcAgwHViQMO+3wKXh75cC13q2ywKWA7sBucA8YK+Wtgf2CtfLA8aE22eFy94B9gcEeAY4Lpw/j+CLy9XAieHy54B+EeRyNJAd/n5tpuXiyyScPzI89kfAwEzKpIVr5XDgBSAv/Dw4k3JpJpN/J5TpeODlDMtkKDA9/L0XsCQse0b/e9tCLhn9761NnTdZS2gKUdVXgU1NZp8M3BX+fhdwimfTfYFlqvqhqlYD94fbtbT9ycD9qlqlqiuAZcC+IjIU6K2qb2rwN/7uJsfMAQqBGuAcYJaqbt75s02eLxdV/beq1oYf3wJGeDbttrk0c60AXA9cTPAqYp9umwk0m8s3gd+oalW4zgbPpt02l2YyUaB3+HsfwPdS7+6cyTpVnRP+vg14HxjewnklyrhcMv3fW9N5rBKa+oao6joI/oEABnvWGQ58nPB5dTivpe2b22Z4+LtvX78n+AdoEPAG8GXgL206q451PsE35aYyKhcROQlYo6rzWlgtozIJjQcOFpG3ReQVEdnHs06m5fID4Hci8nFYpss862REJiIyGpgGvI39e9ugSS6J7N9b02GsEto9+F5D0doAsM1t0+y+VPUfqjpNVc8GfgTcCBwnIg+LyPUi0uXXk4j8H1AL3Otb7JnXLXMRkULg/4Cft7aqZ163zCRBNtAPmAn8BHiwvn9ZgkzL5ZvAD1V1JPBD4HbPOt0+ExHpCTwC/EBVS5PdzDMvI3Kxf29NR7M/rNS3Prw1QfjTdytxNUFfwHoj2HF7rbntm9tmNY1vtSTui3A/w4B9VPUJ4HLgi0AVcOTOnlx7iMiXgROAs8JbNk1lUi67E/SpmiciK8PyzRGRXZqsl0mZ1FsNPKqBd4A47ptJMi2XLwOPhr8/RHArtalunYmI5BBUtO5V1fosMv7f22ZysX9vTaewSmjqe5LgfxiEP5/wrPM/YJyIjAmfFjwj3K6l7Z8EzhCRPBEZA4wD3glvlWwTkZlha9G5nmP+EvhZ+HsBwTfUOEEfnS4hIscClwAnqWp5M6tlTC6qOl9VB6vqaFUdTfCP+HRV/aTJqhmTSYLHgSMARGQ8wUMTTV8dmGm5rAUODX8/AljqWafbZhKW4XbgfVVNfKo6o/+9bS4X+/fWdBpNgaejbAom4D5gHUFn69XABcAA4EWC/0m8CPQP1x1G0Bm7ftvjCZ5kXA78X8J87/bhsv8L1/+A8MnDcH4RsCBcdhPh613DZdOA2xM+/wBYCDxL+PRxF+WyjKAv0dxwujmTcvFl0mT5SsKn4zMlkxaulVzgnrCcc4AjMimXZjI5CCgmeNL4bWBGhmVyEEGl5T12/BtyfHPnZblk9r+3NnXeZO+ON8YYY4wxXc5uxxtjjDHGmC5nlVBjjDHGGNPlrBJqjDHGGGO6nFVCjTHGGGNMl7NKqDHGGGOM6XJWCTUmRYhInYjMTZgu7YJj9hWRb7VhuytF5KJm5q8Jy79IRM5MWHaViBzVwj7vFJHTdrYsCdufLyLzReQ9EVkgIieH888LB7buECLysogUtWP7w0TkX83M3yoi74rIByLyqoic0I7jXCgi57ayzikislfC5xb/jHby+NNE/r+984/Vsizj+OerMME6iBaaWZsOHBIERxBKE13RzFqWKApGLaysaEpji61SKiyHpcV0OgwIqUwgFJJlUsEfAkaBIIdDgZKBs9U0XCLIoRVe/XFdD+fm5QVeDA8ndn22d+d57p/XfT/POe/3XPf9PJdmxfE4SSZpRJE/MtJGxX0ztaZ+s6RNcbxU0qlHw64kSToPXY61AUmS7KPNzJo7uM+ewJc4urGXp5nZnZLOBdZKesjM/m1mhwsp+rqR9A78fYODzWyHPOxgr8geh79v8G8Hqf6GIulEM9vbYPEVZvbRqNcM/EJSm5ktO9J+zey+BopdCfwS+FPUOZrX6OvAd4rzVuA6/D2R4C8zb4njuXg88jKG/RjgwTj+KX6f3nYU7UuS5BiTntAk6cRIOiW8Yn3jfK6kG+J4l6TvS1onaZmkXpHeW9ISSWslrZB0XqSfIWmRpJb4XATcDvQOz+UdUW6SpDXhUZxS2HJz2LIU6Hs4281sC7Abj9u+n6dT0u3hKd0g6c464/52lG/0b9TpwE5gV/S9y8y2Rn8XAD+LMXaX9I0Y30ZJMyIiS+Xh/K6k1ZKekTQ80rtLmhe2zsejs1R2Tpf0pKQ/1szVtuhnJXCNpMslbY7zqxoZkJmtB24Fbow2e0l6OGxfI+l9kk6IvnoWff85rvU+b7WkG6JOS7Rxclz/jwF3xNz0rrlGI8Ir2ypptqSTirFNifuutbq/SiQ1AQPNrKVIXgEMk9Q1/knog7/4HDN7GnhZ0nuK8tcC8+J4MS5gkyQ5jkgRmiSdh+7afzl+tJntwEXIHEljgFPNbGaUfxOwzswGA48D34z0GcBNZjYE+ArtXs67gcfNbBAwGI8w8lXgWTNrNrNJki7DQ+cNA5qBIZIukTQE90ydj4uooYcbjKTBwBYze7Em/TRgJNDfzAayv7cMSd/DReX1ZvZaIxOHe9ReALZKul/SFQBm9hDwJB7vutnM2oB7zGyomQ3ABWW55N3FzIbhEViq+RwP7A5bbwOGFOVvNrMLgIHApZIGFnl7zOxiPGzoTOAKYDjwtgbHBB7hqRJ5d+Fe5qHA1cCsmJ9H8PkkRNw2M3uhpp2FMeZBwCY8wtbvcHE3Kebm2aqwpG7AHGC0mb0bXzUbX7S3Pe676fg9VksV7abEgKXAh4CP0x7SsWIufo8h6b3AS/GPDGb2T+AkSW+pP01Jkvw/kiI0SToPbSEGqs98ADP7Lb6UeS/wuaL8a8D8OH4AuDg8TBcBCyStB34InBllPoCLBsxsbwjcWi6Lz1O0C6BzcfG0yMx2m9krHCggSiZKehoPB/mtOvmvAHuAWZKuwr2lFZOBnmb2BTuCcG6x3H05MAoPGzhNUr2+Ad4v6Q+SWvE56V/kLYyfa4Gz4/gSfH4xsw14SMOKayWtw+erP/CuIq+6NucBW81sS4zpgUbHBag4/iBwT1zXxUCP8DjOB0ZHmTFFvyUDwiveCoxl/zHXo2/Y/Eyc/xifh4p681RyJvCPOunzwsYxuOiszRsV3u96+S/iYSKTJDlOSBGaJJ2c+FLuB7QBpx2iqOG/0y/XiNl+R9IdMLWo28fMflS03wjTzKwvLox+El61diPN/oN7Wh/G9yQuKbLX4N7XA8Yp6Z2Fl/iLtfnmrDazqbiIubpOG91wz/Co8PDNBEr7/hU/97L/nvkDxi7pHNwLOCK8pI/WtPXqoeo3yPm45xL82l5YXJuzzGwnsAroI9+OcSXtArFkDnBjjHlKjZ310GHyDzZPFW31+jCz1cAA4K2FwK3ynge2AZfi1+7nNdW7RbtJkhwnpAhNks7PRFyIXAfMltQ10k/APX8AnwBWhpdyq6RrAOQMijLLiCVVSSdK6oHvo2wq+vo18JnwqCLpLEmnA8uBkbE/sglfWj4kZrYQXwr/dJkebZ9iZr/Cl72bi+wl+D7VR6Ofsr3nCwG230M3kt4ey/8VzcBzcVyOsRJG28OORp7GX457D5E0AF96B+iBC80dks4APnyQ+puBcyT1jvOG9jbG0v5k3AMO8Btif2jkN4OLb2AR8ANgk5m9VKe5JuDvce+MLdJrr39p89mS+sT5p/AtH42yCd/zWY+v4Q8t1WMuMA3fIvLXKlGS8G0M247AhiRJOjn5dHySdB66x1JrxRJgNr4EP8zMdkpaDtyC71d8FegvaS2wg/Yl2bHAdEm3AF3xZc4W4MvADEmfxT1Y481slaQnJG0EHot9of2AVf69zy7gk2a2Tv5Qznpc3K1ocEy3Ag9KmlmkNQGPhFdSuMjeh5ktCAG6WNJHYh/n4egK3Cl/FdMefCm48pbOAe6T1AZciHs/W3FBs6aBtqcD90vagI9/ddjZIukpfG/tX4An6lU2sz2SPo8L6+3AStwbWI/h0ebJ+PLzhOLJ+AnAvWFHF1wcV2OcH2MZd5B2J+PbI57Dx14Jz3nATEkTKAR52Hw9vq2jS7TdyNP2Vf3N8ofqmsJbW+Y9doiqC/C9rzfVpA8Bfh9e9CRJjhN0BNuukiTpREjaZWZvPtZ2JEk9JE0EdprZrKPQ1l3A4tfzqqokSTovuRyfJEmSvBFMp33v6P/KxhSgSXL8kZ7QJEmSJEmSpMNJT2iSJEmSJEnS4aQITZIkSZIkSTqcFKFJkiRJkiRJh5MiNEmSJEmSJOlwUoQmSZIkSZIkHc5/Ab8dGxr7BII6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the efficient frontier\n",
    "\n",
    "label = 'Max Risk Adjusted Return Portfolio' # Title of point\n",
    "mu = port.mu_fm # Expected returns\n",
    "cov = port.cov_fm # Covariance matrix\n",
    "returns = port.returns_fm # Returns of the assets\n",
    "\n",
    "ax = plf.plot_frontier(w_frontier=frontier, mu=mu, cov=cov, returns=returns, rm=rm,\n",
    "                       rf=rf, alpha=0.01, cmap='viridis', w=w, label=label,\n",
    "                       marker='*', s=16, c='r', height=6, width=10, ax=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting efficient frontier composition\n",
    "\n",
    "ax = plf.plot_frontier_area(w_frontier=frontier, cmap=\"tab20\", height=6, width=10, ax=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Estimating Portfolios Using Risk Factors with Other Risk Measures and PCR\n",
    "\n",
    "In this part I will calculate optimal portfolios for several risk measures using a __mean estimate based on PCR__. I will find the portfolios that maximize the risk adjusted return for all available risk measures.\n",
    "\n",
    "### 3.1 Calculate Optimal Portfolios for Several Risk Measures.\n",
    "\n",
    "I will mantain the constraints on risk factors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Risk Measures available:\n",
    "#\n",
    "# 'MV': Standard Deviation.\n",
    "# 'MAD': Mean Absolute Deviation.\n",
    "# 'MSV': Semi Standard Deviation.\n",
    "# 'FLPM': First Lower Partial Moment (Omega Ratio).\n",
    "# 'SLPM': Second Lower Partial Moment (Sortino Ratio).\n",
    "# 'CVaR': Conditional Value at Risk.\n",
    "# 'EVaR': Entropic Value at Risk.\n",
    "# 'WR': Worst Realization (Minimax)\n",
    "# 'MDD': Maximum Drawdown of uncompounded cumulative returns (Calmar Ratio).\n",
    "# 'ADD': Average Drawdown of uncompounded cumulative returns.\n",
    "# 'CDaR': Conditional Drawdown at Risk of uncompounded cumulative returns.\n",
    "# 'EDaR': Entropic Drawdown at Risk of uncompounded cumulative returns.\n",
    "# 'UCI': Ulcer Index of uncompounded cumulative returns.\n",
    "\n",
    "rms = ['MV', 'MAD', 'MSV', 'FLPM', 'SLPM', 'CVaR',\n",
    "       'EVaR', 'WR', 'MDD', 'ADD', 'CDaR', 'UCI', 'EDaR']\n",
    "\n",
    "w_s = pd.DataFrame([])\n",
    "\n",
    "# When we use hist = True the risk measures all calculated\n",
    "# using historical returns, while when hist = False the\n",
    "# risk measures are calculated using the expected returns \n",
    "#  based on risk factor model: R = a + B * F\n",
    "\n",
    "hist = False\n",
    "\n",
    "for i in rms:\n",
    "    w = port.optimization(model=model, rm=i, obj=obj, rf=rf, l=l, hist=hist)\n",
    "    w_s = pd.concat([w_s, w], axis=1)\n",
    "    \n",
    "w_s.columns = rms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_57c4d_row0_col0,#T_57c4d_row0_col1,#T_57c4d_row0_col2,#T_57c4d_row0_col3,#T_57c4d_row0_col4,#T_57c4d_row0_col5,#T_57c4d_row0_col6,#T_57c4d_row0_col7,#T_57c4d_row0_col8,#T_57c4d_row0_col9,#T_57c4d_row0_col10,#T_57c4d_row0_col11,#T_57c4d_row0_col12,#T_57c4d_row1_col1,#T_57c4d_row1_col2,#T_57c4d_row1_col3,#T_57c4d_row1_col4,#T_57c4d_row1_col5,#T_57c4d_row1_col6,#T_57c4d_row1_col7,#T_57c4d_row1_col8,#T_57c4d_row1_col9,#T_57c4d_row1_col10,#T_57c4d_row1_col11,#T_57c4d_row1_col12,#T_57c4d_row2_col1,#T_57c4d_row2_col2,#T_57c4d_row2_col3,#T_57c4d_row2_col4,#T_57c4d_row2_col5,#T_57c4d_row2_col6,#T_57c4d_row2_col7,#T_57c4d_row2_col8,#T_57c4d_row2_col9,#T_57c4d_row2_col10,#T_57c4d_row2_col11,#T_57c4d_row2_col12,#T_57c4d_row3_col0,#T_57c4d_row3_col1,#T_57c4d_row3_col2,#T_57c4d_row3_col3,#T_57c4d_row3_col4,#T_57c4d_row3_col5,#T_57c4d_row3_col6,#T_57c4d_row3_col7,#T_57c4d_row3_col8,#T_57c4d_row3_col9,#T_57c4d_row3_col10,#T_57c4d_row3_col11,#T_57c4d_row3_col12,#T_57c4d_row4_col0,#T_57c4d_row4_col1,#T_57c4d_row4_col2,#T_57c4d_row4_col3,#T_57c4d_row4_col4,#T_57c4d_row4_col5,#T_57c4d_row4_col6,#T_57c4d_row4_col7,#T_57c4d_row4_col8,#T_57c4d_row4_col9,#T_57c4d_row4_col10,#T_57c4d_row4_col11,#T_57c4d_row4_col12,#T_57c4d_row5_col1,#T_57c4d_row5_col2,#T_57c4d_row5_col3,#T_57c4d_row5_col4,#T_57c4d_row5_col5,#T_57c4d_row5_col6,#T_57c4d_row5_col7,#T_57c4d_row5_col8,#T_57c4d_row5_col9,#T_57c4d_row5_col10,#T_57c4d_row5_col11,#T_57c4d_row5_col12,#T_57c4d_row6_col0,#T_57c4d_row6_col1,#T_57c4d_row6_col2,#T_57c4d_row6_col3,#T_57c4d_row6_col4,#T_57c4d_row6_col5,#T_57c4d_row6_col6,#T_57c4d_row6_col7,#T_57c4d_row6_col8,#T_57c4d_row6_col9,#T_57c4d_row6_col10,#T_57c4d_row6_col11,#T_57c4d_row6_col12,#T_57c4d_row7_col1,#T_57c4d_row7_col2,#T_57c4d_row7_col3,#T_57c4d_row7_col4,#T_57c4d_row7_col5,#T_57c4d_row7_col6,#T_57c4d_row7_col7,#T_57c4d_row7_col8,#T_57c4d_row7_col9,#T_57c4d_row7_col10,#T_57c4d_row7_col11,#T_57c4d_row7_col12,#T_57c4d_row8_col0,#T_57c4d_row8_col1,#T_57c4d_row8_col2,#T_57c4d_row8_col3,#T_57c4d_row8_col4,#T_57c4d_row8_col5,#T_57c4d_row8_col6,#T_57c4d_row8_col7,#T_57c4d_row8_col8,#T_57c4d_row8_col9,#T_57c4d_row8_col10,#T_57c4d_row8_col11,#T_57c4d_row8_col12,#T_57c4d_row9_col0,#T_57c4d_row9_col1,#T_57c4d_row9_col2,#T_57c4d_row9_col3,#T_57c4d_row9_col4,#T_57c4d_row9_col5,#T_57c4d_row9_col6,#T_57c4d_row9_col7,#T_57c4d_row9_col8,#T_57c4d_row9_col9,#T_57c4d_row9_col10,#T_57c4d_row9_col11,#T_57c4d_row9_col12,#T_57c4d_row10_col1,#T_57c4d_row10_col2,#T_57c4d_row10_col3,#T_57c4d_row10_col4,#T_57c4d_row10_col5,#T_57c4d_row10_col6,#T_57c4d_row10_col7,#T_57c4d_row10_col8,#T_57c4d_row10_col9,#T_57c4d_row10_col10,#T_57c4d_row10_col11,#T_57c4d_row10_col12,#T_57c4d_row11_col0,#T_57c4d_row11_col1,#T_57c4d_row11_col2,#T_57c4d_row11_col3,#T_57c4d_row11_col4,#T_57c4d_row11_col5,#T_57c4d_row11_col6,#T_57c4d_row11_col7,#T_57c4d_row11_col8,#T_57c4d_row11_col9,#T_57c4d_row11_col10,#T_57c4d_row11_col11,#T_57c4d_row11_col12,#T_57c4d_row12_col1,#T_57c4d_row12_col2,#T_57c4d_row12_col3,#T_57c4d_row12_col4,#T_57c4d_row12_col5,#T_57c4d_row12_col6,#T_57c4d_row12_col7,#T_57c4d_row12_col8,#T_57c4d_row12_col9,#T_57c4d_row12_col10,#T_57c4d_row12_col11,#T_57c4d_row12_col12,#T_57c4d_row13_col0,#T_57c4d_row13_col1,#T_57c4d_row13_col2,#T_57c4d_row13_col3,#T_57c4d_row13_col4,#T_57c4d_row13_col5,#T_57c4d_row13_col6,#T_57c4d_row13_col7,#T_57c4d_row13_col8,#T_57c4d_row13_col9,#T_57c4d_row13_col10,#T_57c4d_row13_col11,#T_57c4d_row13_col12,#T_57c4d_row14_col1,#T_57c4d_row14_col2,#T_57c4d_row14_col3,#T_57c4d_row14_col4,#T_57c4d_row14_col5,#T_57c4d_row14_col6,#T_57c4d_row14_col7,#T_57c4d_row14_col9,#T_57c4d_row14_col10,#T_57c4d_row14_col11,#T_57c4d_row15_col7,#T_57c4d_row16_col0,#T_57c4d_row16_col1,#T_57c4d_row16_col2,#T_57c4d_row16_col3,#T_57c4d_row16_col4,#T_57c4d_row16_col5,#T_57c4d_row16_col6,#T_57c4d_row16_col7,#T_57c4d_row16_col8,#T_57c4d_row16_col9,#T_57c4d_row16_col10,#T_57c4d_row16_col11,#T_57c4d_row16_col12,#T_57c4d_row17_col0,#T_57c4d_row17_col1,#T_57c4d_row17_col2,#T_57c4d_row17_col3,#T_57c4d_row17_col4,#T_57c4d_row17_col5,#T_57c4d_row17_col6,#T_57c4d_row17_col7,#T_57c4d_row17_col8,#T_57c4d_row17_col9,#T_57c4d_row17_col10,#T_57c4d_row17_col11,#T_57c4d_row17_col12,#T_57c4d_row18_col0,#T_57c4d_row18_col1,#T_57c4d_row18_col2,#T_57c4d_row18_col3,#T_57c4d_row18_col4,#T_57c4d_row18_col5,#T_57c4d_row18_col6,#T_57c4d_row18_col7,#T_57c4d_row18_col8,#T_57c4d_row18_col9,#T_57c4d_row18_col10,#T_57c4d_row18_col11,#T_57c4d_row18_col12,#T_57c4d_row19_col0,#T_57c4d_row19_col1,#T_57c4d_row19_col2,#T_57c4d_row19_col3,#T_57c4d_row19_col4,#T_57c4d_row19_col5,#T_57c4d_row19_col6,#T_57c4d_row19_col7,#T_57c4d_row19_col8,#T_57c4d_row19_col9,#T_57c4d_row19_col10,#T_57c4d_row19_col11,#T_57c4d_row19_col12,#T_57c4d_row20_col8,#T_57c4d_row21_col1,#T_57c4d_row21_col2,#T_57c4d_row21_col3,#T_57c4d_row21_col4,#T_57c4d_row21_col5,#T_57c4d_row21_col6,#T_57c4d_row21_col7,#T_57c4d_row21_col8,#T_57c4d_row21_col9,#T_57c4d_row21_col10,#T_57c4d_row21_col11,#T_57c4d_row21_col12,#T_57c4d_row22_col0,#T_57c4d_row22_col1,#T_57c4d_row22_col2,#T_57c4d_row22_col3,#T_57c4d_row22_col4,#T_57c4d_row22_col5,#T_57c4d_row22_col6,#T_57c4d_row22_col7,#T_57c4d_row22_col8,#T_57c4d_row22_col9,#T_57c4d_row22_col10,#T_57c4d_row22_col11,#T_57c4d_row22_col12,#T_57c4d_row23_col1,#T_57c4d_row23_col2,#T_57c4d_row23_col3,#T_57c4d_row23_col4,#T_57c4d_row23_col5,#T_57c4d_row23_col6,#T_57c4d_row23_col7,#T_57c4d_row23_col8,#T_57c4d_row23_col9,#T_57c4d_row23_col10,#T_57c4d_row23_col11,#T_57c4d_row23_col12,#T_57c4d_row24_col0,#T_57c4d_row24_col1,#T_57c4d_row24_col2,#T_57c4d_row24_col3,#T_57c4d_row24_col4,#T_57c4d_row24_col5,#T_57c4d_row24_col6,#T_57c4d_row24_col7,#T_57c4d_row24_col8,#T_57c4d_row24_col9,#T_57c4d_row24_col10,#T_57c4d_row24_col11,#T_57c4d_row24_col12{\n",
       "            background-color:  #ffffe5;\n",
       "            color:  #000000;\n",
       "        }#T_57c4d_row1_col0{\n",
       "            background-color:  #d6efa2;\n",
       "            color:  #000000;\n",
       "        }#T_57c4d_row2_col0{\n",
       "            background-color:  #64bc6f;\n",
       "            color:  #000000;\n",
       "        }#T_57c4d_row5_col0{\n",
       "            background-color:  #72c376;\n",
       "            color:  #000000;\n",
       "        }#T_57c4d_row7_col0{\n",
       "            background-color:  #e0f3a8;\n",
       "            color:  #000000;\n",
       "        }#T_57c4d_row10_col0{\n",
       "            background-color:  #70c275;\n",
       "            color:  #000000;\n",
       "        }#T_57c4d_row12_col0,#T_57c4d_row15_col8,#T_57c4d_row20_col1,#T_57c4d_row20_col2,#T_57c4d_row20_col3,#T_57c4d_row20_col4,#T_57c4d_row20_col5,#T_57c4d_row20_col6,#T_57c4d_row20_col7,#T_57c4d_row20_col9,#T_57c4d_row20_col10,#T_57c4d_row20_col11,#T_57c4d_row20_col12{\n",
       "            background-color:  #004529;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_57c4d_row14_col0{\n",
       "            background-color:  #228343;\n",
       "            color:  #000000;\n",
       "        }#T_57c4d_row14_col8{\n",
       "            background-color:  #14783e;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_57c4d_row14_col12{\n",
       "            background-color:  #e4f4ab;\n",
       "            color:  #000000;\n",
       "        }#T_57c4d_row15_col0{\n",
       "            background-color:  #68be71;\n",
       "            color:  #000000;\n",
       "        }#T_57c4d_row15_col1{\n",
       "            background-color:  #b6e192;\n",
       "            color:  #000000;\n",
       "        }#T_57c4d_row15_col2{\n",
       "            background-color:  #9cd687;\n",
       "            color:  #000000;\n",
       "        }#T_57c4d_row15_col3{\n",
       "            background-color:  #c0e597;\n",
       "            color:  #000000;\n",
       "        }#T_57c4d_row15_col4{\n",
       "            background-color:  #97d385;\n",
       "            color:  #000000;\n",
       "        }#T_57c4d_row15_col5{\n",
       "            background-color:  #39a056;\n",
       "            color:  #000000;\n",
       "        }#T_57c4d_row15_col6{\n",
       "            background-color:  #e6f5ac;\n",
       "            color:  #000000;\n",
       "        }#T_57c4d_row15_col9,#T_57c4d_row15_col10{\n",
       "            background-color:  #8dcf81;\n",
       "            color:  #000000;\n",
       "        }#T_57c4d_row15_col11{\n",
       "            background-color:  #84cb7e;\n",
       "            color:  #000000;\n",
       "        }#T_57c4d_row15_col12{\n",
       "            background-color:  #30954f;\n",
       "            color:  #000000;\n",
       "        }#T_57c4d_row20_col0{\n",
       "            background-color:  #daf0a4;\n",
       "            color:  #000000;\n",
       "        }#T_57c4d_row21_col0{\n",
       "            background-color:  #fcfed7;\n",
       "            color:  #000000;\n",
       "        }#T_57c4d_row23_col0{\n",
       "            background-color:  #ceeb9e;\n",
       "            color:  #000000;\n",
       "        }</style><table id=\"T_57c4d_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >MV</th>        <th class=\"col_heading level0 col1\" >MAD</th>        <th class=\"col_heading level0 col2\" >MSV</th>        <th class=\"col_heading level0 col3\" >FLPM</th>        <th class=\"col_heading level0 col4\" >SLPM</th>        <th class=\"col_heading level0 col5\" >CVaR</th>        <th class=\"col_heading level0 col6\" >EVaR</th>        <th class=\"col_heading level0 col7\" >WR</th>        <th class=\"col_heading level0 col8\" >MDD</th>        <th class=\"col_heading level0 col9\" >ADD</th>        <th class=\"col_heading level0 col10\" >CDaR</th>        <th class=\"col_heading level0 col11\" >UCI</th>        <th class=\"col_heading level0 col12\" >EDaR</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_57c4d_level0_row0\" class=\"row_heading level0 row0\" >APA</th>\n",
       "                        <td id=\"T_57c4d_row0_col0\" class=\"data row0 col0\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row0_col1\" class=\"data row0 col1\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row0_col2\" class=\"data row0 col2\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row0_col3\" class=\"data row0 col3\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row0_col4\" class=\"data row0 col4\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row0_col5\" class=\"data row0 col5\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row0_col6\" class=\"data row0 col6\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row0_col7\" class=\"data row0 col7\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row0_col8\" class=\"data row0 col8\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row0_col9\" class=\"data row0 col9\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row0_col10\" class=\"data row0 col10\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row0_col11\" class=\"data row0 col11\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row0_col12\" class=\"data row0 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_57c4d_level0_row1\" class=\"row_heading level0 row1\" >BA</th>\n",
       "                        <td id=\"T_57c4d_row1_col0\" class=\"data row1 col0\" >5.25%</td>\n",
       "                        <td id=\"T_57c4d_row1_col1\" class=\"data row1 col1\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row1_col2\" class=\"data row1 col2\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row1_col3\" class=\"data row1 col3\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row1_col4\" class=\"data row1 col4\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row1_col5\" class=\"data row1 col5\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row1_col6\" class=\"data row1 col6\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row1_col7\" class=\"data row1 col7\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row1_col8\" class=\"data row1 col8\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row1_col9\" class=\"data row1 col9\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row1_col10\" class=\"data row1 col10\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row1_col11\" class=\"data row1 col11\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row1_col12\" class=\"data row1 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_57c4d_level0_row2\" class=\"row_heading level0 row2\" >BAX</th>\n",
       "                        <td id=\"T_57c4d_row2_col0\" class=\"data row2 col0\" >11.06%</td>\n",
       "                        <td id=\"T_57c4d_row2_col1\" class=\"data row2 col1\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row2_col2\" class=\"data row2 col2\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row2_col3\" class=\"data row2 col3\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row2_col4\" class=\"data row2 col4\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row2_col5\" class=\"data row2 col5\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row2_col6\" class=\"data row2 col6\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row2_col7\" class=\"data row2 col7\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row2_col8\" class=\"data row2 col8\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row2_col9\" class=\"data row2 col9\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row2_col10\" class=\"data row2 col10\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row2_col11\" class=\"data row2 col11\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row2_col12\" class=\"data row2 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_57c4d_level0_row3\" class=\"row_heading level0 row3\" >BMY</th>\n",
       "                        <td id=\"T_57c4d_row3_col0\" class=\"data row3 col0\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row3_col1\" class=\"data row3 col1\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row3_col2\" class=\"data row3 col2\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row3_col3\" class=\"data row3 col3\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row3_col4\" class=\"data row3 col4\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row3_col5\" class=\"data row3 col5\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row3_col6\" class=\"data row3 col6\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row3_col7\" class=\"data row3 col7\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row3_col8\" class=\"data row3 col8\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row3_col9\" class=\"data row3 col9\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row3_col10\" class=\"data row3 col10\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row3_col11\" class=\"data row3 col11\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row3_col12\" class=\"data row3 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_57c4d_level0_row4\" class=\"row_heading level0 row4\" >CMCSA</th>\n",
       "                        <td id=\"T_57c4d_row4_col0\" class=\"data row4 col0\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row4_col1\" class=\"data row4 col1\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row4_col2\" class=\"data row4 col2\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row4_col3\" class=\"data row4 col3\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row4_col4\" class=\"data row4 col4\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row4_col5\" class=\"data row4 col5\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row4_col6\" class=\"data row4 col6\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row4_col7\" class=\"data row4 col7\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row4_col8\" class=\"data row4 col8\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row4_col9\" class=\"data row4 col9\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row4_col10\" class=\"data row4 col10\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row4_col11\" class=\"data row4 col11\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row4_col12\" class=\"data row4 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_57c4d_level0_row5\" class=\"row_heading level0 row5\" >CNP</th>\n",
       "                        <td id=\"T_57c4d_row5_col0\" class=\"data row5 col0\" >10.45%</td>\n",
       "                        <td id=\"T_57c4d_row5_col1\" class=\"data row5 col1\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row5_col2\" class=\"data row5 col2\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row5_col3\" class=\"data row5 col3\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row5_col4\" class=\"data row5 col4\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row5_col5\" class=\"data row5 col5\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row5_col6\" class=\"data row5 col6\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row5_col7\" class=\"data row5 col7\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row5_col8\" class=\"data row5 col8\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row5_col9\" class=\"data row5 col9\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row5_col10\" class=\"data row5 col10\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row5_col11\" class=\"data row5 col11\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row5_col12\" class=\"data row5 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_57c4d_level0_row6\" class=\"row_heading level0 row6\" >CPB</th>\n",
       "                        <td id=\"T_57c4d_row6_col0\" class=\"data row6 col0\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row6_col1\" class=\"data row6 col1\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row6_col2\" class=\"data row6 col2\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row6_col3\" class=\"data row6 col3\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row6_col4\" class=\"data row6 col4\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row6_col5\" class=\"data row6 col5\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row6_col6\" class=\"data row6 col6\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row6_col7\" class=\"data row6 col7\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row6_col8\" class=\"data row6 col8\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row6_col9\" class=\"data row6 col9\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row6_col10\" class=\"data row6 col10\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row6_col11\" class=\"data row6 col11\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row6_col12\" class=\"data row6 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_57c4d_level0_row7\" class=\"row_heading level0 row7\" >DE</th>\n",
       "                        <td id=\"T_57c4d_row7_col0\" class=\"data row7 col0\" >4.50%</td>\n",
       "                        <td id=\"T_57c4d_row7_col1\" class=\"data row7 col1\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row7_col2\" class=\"data row7 col2\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row7_col3\" class=\"data row7 col3\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row7_col4\" class=\"data row7 col4\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row7_col5\" class=\"data row7 col5\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row7_col6\" class=\"data row7 col6\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row7_col7\" class=\"data row7 col7\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row7_col8\" class=\"data row7 col8\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row7_col9\" class=\"data row7 col9\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row7_col10\" class=\"data row7 col10\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row7_col11\" class=\"data row7 col11\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row7_col12\" class=\"data row7 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_57c4d_level0_row8\" class=\"row_heading level0 row8\" >HPQ</th>\n",
       "                        <td id=\"T_57c4d_row8_col0\" class=\"data row8 col0\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row8_col1\" class=\"data row8 col1\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row8_col2\" class=\"data row8 col2\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row8_col3\" class=\"data row8 col3\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row8_col4\" class=\"data row8 col4\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row8_col5\" class=\"data row8 col5\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row8_col6\" class=\"data row8 col6\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row8_col7\" class=\"data row8 col7\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row8_col8\" class=\"data row8 col8\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row8_col9\" class=\"data row8 col9\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row8_col10\" class=\"data row8 col10\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row8_col11\" class=\"data row8 col11\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row8_col12\" class=\"data row8 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_57c4d_level0_row9\" class=\"row_heading level0 row9\" >JCI</th>\n",
       "                        <td id=\"T_57c4d_row9_col0\" class=\"data row9 col0\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row9_col1\" class=\"data row9 col1\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row9_col2\" class=\"data row9 col2\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row9_col3\" class=\"data row9 col3\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row9_col4\" class=\"data row9 col4\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row9_col5\" class=\"data row9 col5\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row9_col6\" class=\"data row9 col6\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row9_col7\" class=\"data row9 col7\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row9_col8\" class=\"data row9 col8\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row9_col9\" class=\"data row9 col9\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row9_col10\" class=\"data row9 col10\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row9_col11\" class=\"data row9 col11\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row9_col12\" class=\"data row9 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_57c4d_level0_row10\" class=\"row_heading level0 row10\" >JPM</th>\n",
       "                        <td id=\"T_57c4d_row10_col0\" class=\"data row10 col0\" >10.52%</td>\n",
       "                        <td id=\"T_57c4d_row10_col1\" class=\"data row10 col1\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row10_col2\" class=\"data row10 col2\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row10_col3\" class=\"data row10 col3\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row10_col4\" class=\"data row10 col4\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row10_col5\" class=\"data row10 col5\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row10_col6\" class=\"data row10 col6\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row10_col7\" class=\"data row10 col7\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row10_col8\" class=\"data row10 col8\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row10_col9\" class=\"data row10 col9\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row10_col10\" class=\"data row10 col10\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row10_col11\" class=\"data row10 col11\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row10_col12\" class=\"data row10 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_57c4d_level0_row11\" class=\"row_heading level0 row11\" >LUV</th>\n",
       "                        <td id=\"T_57c4d_row11_col0\" class=\"data row11 col0\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row11_col1\" class=\"data row11 col1\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row11_col2\" class=\"data row11 col2\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row11_col3\" class=\"data row11 col3\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row11_col4\" class=\"data row11 col4\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row11_col5\" class=\"data row11 col5\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row11_col6\" class=\"data row11 col6\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row11_col7\" class=\"data row11 col7\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row11_col8\" class=\"data row11 col8\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row11_col9\" class=\"data row11 col9\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row11_col10\" class=\"data row11 col10\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row11_col11\" class=\"data row11 col11\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row11_col12\" class=\"data row11 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_57c4d_level0_row12\" class=\"row_heading level0 row12\" >MMC</th>\n",
       "                        <td id=\"T_57c4d_row12_col0\" class=\"data row12 col0\" >20.35%</td>\n",
       "                        <td id=\"T_57c4d_row12_col1\" class=\"data row12 col1\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row12_col2\" class=\"data row12 col2\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row12_col3\" class=\"data row12 col3\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row12_col4\" class=\"data row12 col4\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row12_col5\" class=\"data row12 col5\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row12_col6\" class=\"data row12 col6\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row12_col7\" class=\"data row12 col7\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row12_col8\" class=\"data row12 col8\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row12_col9\" class=\"data row12 col9\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row12_col10\" class=\"data row12 col10\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row12_col11\" class=\"data row12 col11\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row12_col12\" class=\"data row12 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_57c4d_level0_row13\" class=\"row_heading level0 row13\" >MO</th>\n",
       "                        <td id=\"T_57c4d_row13_col0\" class=\"data row13 col0\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row13_col1\" class=\"data row13 col1\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row13_col2\" class=\"data row13 col2\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row13_col3\" class=\"data row13 col3\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row13_col4\" class=\"data row13 col4\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row13_col5\" class=\"data row13 col5\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row13_col6\" class=\"data row13 col6\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row13_col7\" class=\"data row13 col7\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row13_col8\" class=\"data row13 col8\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row13_col9\" class=\"data row13 col9\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row13_col10\" class=\"data row13 col10\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row13_col11\" class=\"data row13 col11\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row13_col12\" class=\"data row13 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_57c4d_level0_row14\" class=\"row_heading level0 row14\" >MSFT</th>\n",
       "                        <td id=\"T_57c4d_row14_col0\" class=\"data row14 col0\" >15.30%</td>\n",
       "                        <td id=\"T_57c4d_row14_col1\" class=\"data row14 col1\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row14_col2\" class=\"data row14 col2\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row14_col3\" class=\"data row14 col3\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row14_col4\" class=\"data row14 col4\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row14_col5\" class=\"data row14 col5\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row14_col6\" class=\"data row14 col6\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row14_col7\" class=\"data row14 col7\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row14_col8\" class=\"data row14 col8\" >44.55%</td>\n",
       "                        <td id=\"T_57c4d_row14_col9\" class=\"data row14 col9\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row14_col10\" class=\"data row14 col10\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row14_col11\" class=\"data row14 col11\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row14_col12\" class=\"data row14 col12\" >10.71%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_57c4d_level0_row15\" class=\"row_heading level0 row15\" >NI</th>\n",
       "                        <td id=\"T_57c4d_row15_col0\" class=\"data row15 col0\" >10.93%</td>\n",
       "                        <td id=\"T_57c4d_row15_col1\" class=\"data row15 col1\" >25.98%</td>\n",
       "                        <td id=\"T_57c4d_row15_col2\" class=\"data row15 col2\" >29.46%</td>\n",
       "                        <td id=\"T_57c4d_row15_col3\" class=\"data row15 col3\" >24.34%</td>\n",
       "                        <td id=\"T_57c4d_row15_col4\" class=\"data row15 col4\" >29.91%</td>\n",
       "                        <td id=\"T_57c4d_row15_col5\" class=\"data row15 col5\" >39.64%</td>\n",
       "                        <td id=\"T_57c4d_row15_col6\" class=\"data row15 col6\" >16.43%</td>\n",
       "                        <td id=\"T_57c4d_row15_col7\" class=\"data row15 col7\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row15_col8\" class=\"data row15 col8\" >55.45%</td>\n",
       "                        <td id=\"T_57c4d_row15_col9\" class=\"data row15 col9\" >31.07%</td>\n",
       "                        <td id=\"T_57c4d_row15_col10\" class=\"data row15 col10\" >31.07%</td>\n",
       "                        <td id=\"T_57c4d_row15_col11\" class=\"data row15 col11\" >32.09%</td>\n",
       "                        <td id=\"T_57c4d_row15_col12\" class=\"data row15 col12\" >36.60%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_57c4d_level0_row16\" class=\"row_heading level0 row16\" >PCAR</th>\n",
       "                        <td id=\"T_57c4d_row16_col0\" class=\"data row16 col0\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row16_col1\" class=\"data row16 col1\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row16_col2\" class=\"data row16 col2\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row16_col3\" class=\"data row16 col3\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row16_col4\" class=\"data row16 col4\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row16_col5\" class=\"data row16 col5\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row16_col6\" class=\"data row16 col6\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row16_col7\" class=\"data row16 col7\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row16_col8\" class=\"data row16 col8\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row16_col9\" class=\"data row16 col9\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row16_col10\" class=\"data row16 col10\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row16_col11\" class=\"data row16 col11\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row16_col12\" class=\"data row16 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_57c4d_level0_row17\" class=\"row_heading level0 row17\" >PSA</th>\n",
       "                        <td id=\"T_57c4d_row17_col0\" class=\"data row17 col0\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row17_col1\" class=\"data row17 col1\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row17_col2\" class=\"data row17 col2\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row17_col3\" class=\"data row17 col3\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row17_col4\" class=\"data row17 col4\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row17_col5\" class=\"data row17 col5\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row17_col6\" class=\"data row17 col6\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row17_col7\" class=\"data row17 col7\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row17_col8\" class=\"data row17 col8\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row17_col9\" class=\"data row17 col9\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row17_col10\" class=\"data row17 col10\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row17_col11\" class=\"data row17 col11\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row17_col12\" class=\"data row17 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_57c4d_level0_row18\" class=\"row_heading level0 row18\" >SEE</th>\n",
       "                        <td id=\"T_57c4d_row18_col0\" class=\"data row18 col0\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row18_col1\" class=\"data row18 col1\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row18_col2\" class=\"data row18 col2\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row18_col3\" class=\"data row18 col3\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row18_col4\" class=\"data row18 col4\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row18_col5\" class=\"data row18 col5\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row18_col6\" class=\"data row18 col6\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row18_col7\" class=\"data row18 col7\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row18_col8\" class=\"data row18 col8\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row18_col9\" class=\"data row18 col9\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row18_col10\" class=\"data row18 col10\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row18_col11\" class=\"data row18 col11\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row18_col12\" class=\"data row18 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_57c4d_level0_row19\" class=\"row_heading level0 row19\" >T</th>\n",
       "                        <td id=\"T_57c4d_row19_col0\" class=\"data row19 col0\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row19_col1\" class=\"data row19 col1\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row19_col2\" class=\"data row19 col2\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row19_col3\" class=\"data row19 col3\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row19_col4\" class=\"data row19 col4\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row19_col5\" class=\"data row19 col5\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row19_col6\" class=\"data row19 col6\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row19_col7\" class=\"data row19 col7\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row19_col8\" class=\"data row19 col8\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row19_col9\" class=\"data row19 col9\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row19_col10\" class=\"data row19 col10\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row19_col11\" class=\"data row19 col11\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row19_col12\" class=\"data row19 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_57c4d_level0_row20\" class=\"row_heading level0 row20\" >TGT</th>\n",
       "                        <td id=\"T_57c4d_row20_col0\" class=\"data row20 col0\" >5.08%</td>\n",
       "                        <td id=\"T_57c4d_row20_col1\" class=\"data row20 col1\" >74.02%</td>\n",
       "                        <td id=\"T_57c4d_row20_col2\" class=\"data row20 col2\" >70.54%</td>\n",
       "                        <td id=\"T_57c4d_row20_col3\" class=\"data row20 col3\" >75.66%</td>\n",
       "                        <td id=\"T_57c4d_row20_col4\" class=\"data row20 col4\" >70.09%</td>\n",
       "                        <td id=\"T_57c4d_row20_col5\" class=\"data row20 col5\" >60.36%</td>\n",
       "                        <td id=\"T_57c4d_row20_col6\" class=\"data row20 col6\" >83.57%</td>\n",
       "                        <td id=\"T_57c4d_row20_col7\" class=\"data row20 col7\" >100.00%</td>\n",
       "                        <td id=\"T_57c4d_row20_col8\" class=\"data row20 col8\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row20_col9\" class=\"data row20 col9\" >68.93%</td>\n",
       "                        <td id=\"T_57c4d_row20_col10\" class=\"data row20 col10\" >68.93%</td>\n",
       "                        <td id=\"T_57c4d_row20_col11\" class=\"data row20 col11\" >67.91%</td>\n",
       "                        <td id=\"T_57c4d_row20_col12\" class=\"data row20 col12\" >52.69%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_57c4d_level0_row21\" class=\"row_heading level0 row21\" >TMO</th>\n",
       "                        <td id=\"T_57c4d_row21_col0\" class=\"data row21 col0\" >0.80%</td>\n",
       "                        <td id=\"T_57c4d_row21_col1\" class=\"data row21 col1\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row21_col2\" class=\"data row21 col2\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row21_col3\" class=\"data row21 col3\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row21_col4\" class=\"data row21 col4\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row21_col5\" class=\"data row21 col5\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row21_col6\" class=\"data row21 col6\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row21_col7\" class=\"data row21 col7\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row21_col8\" class=\"data row21 col8\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row21_col9\" class=\"data row21 col9\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row21_col10\" class=\"data row21 col10\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row21_col11\" class=\"data row21 col11\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row21_col12\" class=\"data row21 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_57c4d_level0_row22\" class=\"row_heading level0 row22\" >TXT</th>\n",
       "                        <td id=\"T_57c4d_row22_col0\" class=\"data row22 col0\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row22_col1\" class=\"data row22 col1\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row22_col2\" class=\"data row22 col2\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row22_col3\" class=\"data row22 col3\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row22_col4\" class=\"data row22 col4\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row22_col5\" class=\"data row22 col5\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row22_col6\" class=\"data row22 col6\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row22_col7\" class=\"data row22 col7\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row22_col8\" class=\"data row22 col8\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row22_col9\" class=\"data row22 col9\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row22_col10\" class=\"data row22 col10\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row22_col11\" class=\"data row22 col11\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row22_col12\" class=\"data row22 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_57c4d_level0_row23\" class=\"row_heading level0 row23\" >VZ</th>\n",
       "                        <td id=\"T_57c4d_row23_col0\" class=\"data row23 col0\" >5.76%</td>\n",
       "                        <td id=\"T_57c4d_row23_col1\" class=\"data row23 col1\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row23_col2\" class=\"data row23 col2\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row23_col3\" class=\"data row23 col3\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row23_col4\" class=\"data row23 col4\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row23_col5\" class=\"data row23 col5\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row23_col6\" class=\"data row23 col6\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row23_col7\" class=\"data row23 col7\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row23_col8\" class=\"data row23 col8\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row23_col9\" class=\"data row23 col9\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row23_col10\" class=\"data row23 col10\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row23_col11\" class=\"data row23 col11\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row23_col12\" class=\"data row23 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_57c4d_level0_row24\" class=\"row_heading level0 row24\" >ZION</th>\n",
       "                        <td id=\"T_57c4d_row24_col0\" class=\"data row24 col0\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row24_col1\" class=\"data row24 col1\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row24_col2\" class=\"data row24 col2\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row24_col3\" class=\"data row24 col3\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row24_col4\" class=\"data row24 col4\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row24_col5\" class=\"data row24 col5\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row24_col6\" class=\"data row24 col6\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row24_col7\" class=\"data row24 col7\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row24_col8\" class=\"data row24 col8\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row24_col9\" class=\"data row24 col9\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row24_col10\" class=\"data row24 col10\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row24_col11\" class=\"data row24 col11\" >0.00%</td>\n",
       "                        <td id=\"T_57c4d_row24_col12\" class=\"data row24 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fdcd7412760>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_s.style.format(\"{:.2%}\").background_gradient(cmap='YlGn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plotting a comparison of assets weights for each portfolio\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig.set_figwidth(14)\n",
    "fig.set_figheight(6)\n",
    "ax = fig.subplots(nrows=1, ncols=1)\n",
    "\n",
    "w_s.plot.bar(ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "w_s = pd.DataFrame([])\n",
    "\n",
    "# When we use hist = True the risk measures all calculated\n",
    "# using historical returns, while when hist = False the\n",
    "# risk measures are calculated using the expected returns \n",
    "# based on risk factor model: R = a + B * F\n",
    "\n",
    "hist = True\n",
    "\n",
    "for i in rms:\n",
    "    w = port.optimization(model=model, rm=i, obj=obj, rf=rf, l=l, hist=hist)\n",
    "    w_s = pd.concat([w_s, w], axis=1)\n",
    "    \n",
    "w_s.columns = rms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_36729_row0_col0,#T_36729_row0_col1,#T_36729_row0_col2,#T_36729_row0_col3,#T_36729_row0_col4,#T_36729_row0_col5,#T_36729_row0_col6,#T_36729_row0_col7,#T_36729_row0_col8,#T_36729_row0_col9,#T_36729_row0_col10,#T_36729_row0_col11,#T_36729_row0_col12,#T_36729_row1_col5,#T_36729_row1_col7,#T_36729_row1_col12,#T_36729_row2_col7,#T_36729_row2_col8,#T_36729_row2_col10,#T_36729_row2_col12,#T_36729_row3_col0,#T_36729_row3_col1,#T_36729_row3_col2,#T_36729_row3_col3,#T_36729_row3_col4,#T_36729_row3_col5,#T_36729_row3_col6,#T_36729_row3_col7,#T_36729_row3_col8,#T_36729_row3_col9,#T_36729_row3_col10,#T_36729_row3_col11,#T_36729_row3_col12,#T_36729_row4_col0,#T_36729_row4_col1,#T_36729_row4_col2,#T_36729_row4_col3,#T_36729_row4_col4,#T_36729_row4_col5,#T_36729_row4_col6,#T_36729_row4_col7,#T_36729_row4_col8,#T_36729_row4_col9,#T_36729_row4_col10,#T_36729_row4_col11,#T_36729_row4_col12,#T_36729_row6_col0,#T_36729_row6_col1,#T_36729_row6_col2,#T_36729_row6_col3,#T_36729_row6_col4,#T_36729_row6_col5,#T_36729_row6_col6,#T_36729_row6_col8,#T_36729_row6_col9,#T_36729_row6_col10,#T_36729_row6_col11,#T_36729_row6_col12,#T_36729_row7_col6,#T_36729_row7_col7,#T_36729_row8_col0,#T_36729_row8_col1,#T_36729_row8_col2,#T_36729_row8_col3,#T_36729_row8_col4,#T_36729_row8_col5,#T_36729_row8_col6,#T_36729_row8_col7,#T_36729_row8_col8,#T_36729_row8_col9,#T_36729_row8_col10,#T_36729_row8_col11,#T_36729_row8_col12,#T_36729_row9_col0,#T_36729_row9_col1,#T_36729_row9_col2,#T_36729_row9_col3,#T_36729_row9_col4,#T_36729_row9_col5,#T_36729_row9_col6,#T_36729_row9_col7,#T_36729_row9_col8,#T_36729_row9_col9,#T_36729_row9_col10,#T_36729_row9_col11,#T_36729_row9_col12,#T_36729_row10_col5,#T_36729_row10_col6,#T_36729_row10_col7,#T_36729_row10_col8,#T_36729_row10_col9,#T_36729_row10_col10,#T_36729_row10_col11,#T_36729_row10_col12,#T_36729_row11_col0,#T_36729_row11_col1,#T_36729_row11_col2,#T_36729_row11_col3,#T_36729_row11_col4,#T_36729_row11_col5,#T_36729_row11_col6,#T_36729_row11_col7,#T_36729_row11_col8,#T_36729_row11_col9,#T_36729_row11_col10,#T_36729_row11_col11,#T_36729_row11_col12,#T_36729_row12_col8,#T_36729_row12_col12,#T_36729_row13_col0,#T_36729_row13_col1,#T_36729_row13_col2,#T_36729_row13_col3,#T_36729_row13_col4,#T_36729_row13_col5,#T_36729_row13_col6,#T_36729_row13_col7,#T_36729_row13_col8,#T_36729_row13_col9,#T_36729_row13_col10,#T_36729_row13_col11,#T_36729_row13_col12,#T_36729_row15_col6,#T_36729_row15_col7,#T_36729_row15_col8,#T_36729_row16_col0,#T_36729_row16_col1,#T_36729_row16_col2,#T_36729_row16_col3,#T_36729_row16_col4,#T_36729_row16_col5,#T_36729_row16_col6,#T_36729_row16_col8,#T_36729_row16_col9,#T_36729_row16_col10,#T_36729_row16_col11,#T_36729_row16_col12,#T_36729_row17_col0,#T_36729_row17_col1,#T_36729_row17_col2,#T_36729_row17_col3,#T_36729_row17_col4,#T_36729_row17_col5,#T_36729_row17_col6,#T_36729_row17_col7,#T_36729_row17_col8,#T_36729_row17_col9,#T_36729_row17_col10,#T_36729_row17_col11,#T_36729_row17_col12,#T_36729_row18_col0,#T_36729_row18_col1,#T_36729_row18_col2,#T_36729_row18_col3,#T_36729_row18_col4,#T_36729_row18_col5,#T_36729_row18_col6,#T_36729_row18_col7,#T_36729_row18_col8,#T_36729_row18_col9,#T_36729_row18_col10,#T_36729_row18_col11,#T_36729_row18_col12,#T_36729_row19_col0,#T_36729_row19_col1,#T_36729_row19_col2,#T_36729_row19_col3,#T_36729_row19_col4,#T_36729_row19_col5,#T_36729_row19_col6,#T_36729_row19_col7,#T_36729_row19_col8,#T_36729_row19_col9,#T_36729_row19_col10,#T_36729_row19_col11,#T_36729_row19_col12,#T_36729_row20_col8,#T_36729_row20_col10,#T_36729_row21_col0,#T_36729_row21_col1,#T_36729_row21_col2,#T_36729_row21_col3,#T_36729_row21_col4,#T_36729_row21_col5,#T_36729_row21_col6,#T_36729_row21_col7,#T_36729_row21_col8,#T_36729_row21_col10,#T_36729_row21_col11,#T_36729_row21_col12,#T_36729_row22_col0,#T_36729_row22_col1,#T_36729_row22_col2,#T_36729_row22_col3,#T_36729_row22_col4,#T_36729_row22_col5,#T_36729_row22_col6,#T_36729_row22_col7,#T_36729_row22_col8,#T_36729_row22_col9,#T_36729_row22_col10,#T_36729_row22_col11,#T_36729_row22_col12,#T_36729_row23_col6,#T_36729_row23_col7,#T_36729_row23_col8,#T_36729_row24_col0,#T_36729_row24_col1,#T_36729_row24_col2,#T_36729_row24_col3,#T_36729_row24_col4,#T_36729_row24_col5,#T_36729_row24_col6,#T_36729_row24_col7,#T_36729_row24_col8,#T_36729_row24_col9,#T_36729_row24_col10,#T_36729_row24_col11,#T_36729_row24_col12{\n",
       "            background-color:  #ffffe5;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row1_col0,#T_36729_row5_col3{\n",
       "            background-color:  #ccea9d;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row1_col1{\n",
       "            background-color:  #b5e092;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row1_col2{\n",
       "            background-color:  #e7f6ad;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row1_col3{\n",
       "            background-color:  #c7e89a;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row1_col4{\n",
       "            background-color:  #ecf7b1;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row1_col6{\n",
       "            background-color:  #fcfed6;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row1_col8{\n",
       "            background-color:  #feffde;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row1_col9,#T_36729_row6_col7,#T_36729_row23_col10{\n",
       "            background-color:  #edf8b1;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row1_col10{\n",
       "            background-color:  #fbfdcf;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row1_col11{\n",
       "            background-color:  #f1fab5;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row2_col0{\n",
       "            background-color:  #69bf72;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row2_col1{\n",
       "            background-color:  #90d083;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row2_col2{\n",
       "            background-color:  #88cd7f;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row2_col3,#T_36729_row2_col5,#T_36729_row5_col11{\n",
       "            background-color:  #97d385;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row2_col4,#T_36729_row15_col2{\n",
       "            background-color:  #86cc7f;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row2_col6,#T_36729_row15_col9{\n",
       "            background-color:  #f6fcb8;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row2_col9{\n",
       "            background-color:  #f7fcbc;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row2_col11{\n",
       "            background-color:  #f8fdc1;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row5_col0,#T_36729_row10_col2{\n",
       "            background-color:  #a6da8b;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row5_col1{\n",
       "            background-color:  #bae394;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row5_col2,#T_36729_row20_col4,#T_36729_row20_col6{\n",
       "            background-color:  #9fd788;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row5_col4{\n",
       "            background-color:  #9dd688;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row5_col5{\n",
       "            background-color:  #d6efa2;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row5_col6{\n",
       "            background-color:  #006536;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_36729_row5_col7{\n",
       "            background-color:  #005d33;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_36729_row5_col8,#T_36729_row12_col0,#T_36729_row12_col5,#T_36729_row12_col7,#T_36729_row14_col1,#T_36729_row14_col2,#T_36729_row14_col3,#T_36729_row14_col4,#T_36729_row14_col6,#T_36729_row14_col9,#T_36729_row14_col10,#T_36729_row14_col11,#T_36729_row14_col12{\n",
       "            background-color:  #004529;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_36729_row5_col9{\n",
       "            background-color:  #b9e294;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row5_col10{\n",
       "            background-color:  #278946;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row5_col12{\n",
       "            background-color:  #11753d;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_36729_row7_col0,#T_36729_row7_col5{\n",
       "            background-color:  #ebf7b0;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row7_col1,#T_36729_row23_col5{\n",
       "            background-color:  #f7fcb9;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row7_col2{\n",
       "            background-color:  #fcfed3;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row7_col3{\n",
       "            background-color:  #f8fcbe;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row7_col4{\n",
       "            background-color:  #fcfed7;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row7_col8{\n",
       "            background-color:  #ddf2a6;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row7_col9{\n",
       "            background-color:  #fdfed9;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row7_col10,#T_36729_row15_col12{\n",
       "            background-color:  #feffdf;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row7_col11{\n",
       "            background-color:  #fdfedd;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row7_col12{\n",
       "            background-color:  #fbfed0;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row10_col0{\n",
       "            background-color:  #a2d88a;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row10_col1{\n",
       "            background-color:  #30954f;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row10_col3{\n",
       "            background-color:  #15793e;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_36729_row10_col4{\n",
       "            background-color:  #aedd8e;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row12_col1{\n",
       "            background-color:  #39a056;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row12_col2{\n",
       "            background-color:  #00542f;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_36729_row12_col3{\n",
       "            background-color:  #349a52;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row12_col4{\n",
       "            background-color:  #00532f;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_36729_row12_col6{\n",
       "            background-color:  #8dcf81;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row12_col9,#T_36729_row15_col5{\n",
       "            background-color:  #92d183;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row12_col10{\n",
       "            background-color:  #edf8b2;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row12_col11{\n",
       "            background-color:  #b3e091;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row14_col0{\n",
       "            background-color:  #10743c;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_36729_row14_col5{\n",
       "            background-color:  #3ca458;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row14_col7,#T_36729_row20_col3,#T_36729_row23_col1{\n",
       "            background-color:  #cfec9e;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row14_col8{\n",
       "            background-color:  #6bc072;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row15_col0{\n",
       "            background-color:  #77c679;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row15_col1{\n",
       "            background-color:  #5db96b;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row15_col3{\n",
       "            background-color:  #4cb063;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row15_col4{\n",
       "            background-color:  #89ce80;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row15_col10{\n",
       "            background-color:  #fafdcb;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row15_col11{\n",
       "            background-color:  #f9fdc5;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row16_col7{\n",
       "            background-color:  #c9e99c;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row20_col0{\n",
       "            background-color:  #bce395;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row20_col1{\n",
       "            background-color:  #e0f3a8;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row20_col2{\n",
       "            background-color:  #a4d98a;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row20_col5{\n",
       "            background-color:  #a7db8c;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row20_col7{\n",
       "            background-color:  #7cc87b;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row20_col9{\n",
       "            background-color:  #fdfeda;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row20_col11{\n",
       "            background-color:  #feffe2;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row20_col12{\n",
       "            background-color:  #ffffe4;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row21_col9,#T_36729_row23_col12{\n",
       "            background-color:  #feffe1;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row23_col0,#T_36729_row23_col11{\n",
       "            background-color:  #e6f5ac;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row23_col2{\n",
       "            background-color:  #f5fbb8;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row23_col3{\n",
       "            background-color:  #c8e99b;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row23_col4{\n",
       "            background-color:  #f8fcbd;\n",
       "            color:  #000000;\n",
       "        }#T_36729_row23_col9{\n",
       "            background-color:  #e4f4ab;\n",
       "            color:  #000000;\n",
       "        }</style><table id=\"T_36729_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >MV</th>        <th class=\"col_heading level0 col1\" >MAD</th>        <th class=\"col_heading level0 col2\" >MSV</th>        <th class=\"col_heading level0 col3\" >FLPM</th>        <th class=\"col_heading level0 col4\" >SLPM</th>        <th class=\"col_heading level0 col5\" >CVaR</th>        <th class=\"col_heading level0 col6\" >EVaR</th>        <th class=\"col_heading level0 col7\" >WR</th>        <th class=\"col_heading level0 col8\" >MDD</th>        <th class=\"col_heading level0 col9\" >ADD</th>        <th class=\"col_heading level0 col10\" >CDaR</th>        <th class=\"col_heading level0 col11\" >UCI</th>        <th class=\"col_heading level0 col12\" >EDaR</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_36729_level0_row0\" class=\"row_heading level0 row0\" >APA</th>\n",
       "                        <td id=\"T_36729_row0_col0\" class=\"data row0 col0\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row0_col1\" class=\"data row0 col1\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row0_col2\" class=\"data row0 col2\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row0_col3\" class=\"data row0 col3\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row0_col4\" class=\"data row0 col4\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row0_col5\" class=\"data row0 col5\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row0_col6\" class=\"data row0 col6\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row0_col7\" class=\"data row0 col7\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row0_col8\" class=\"data row0 col8\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row0_col9\" class=\"data row0 col9\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row0_col10\" class=\"data row0 col10\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row0_col11\" class=\"data row0 col11\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row0_col12\" class=\"data row0 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_36729_level0_row1\" class=\"row_heading level0 row1\" >BA</th>\n",
       "                        <td id=\"T_36729_row1_col0\" class=\"data row1 col0\" >6.16%</td>\n",
       "                        <td id=\"T_36729_row1_col1\" class=\"data row1 col1\" >7.58%</td>\n",
       "                        <td id=\"T_36729_row1_col2\" class=\"data row1 col2\" >4.38%</td>\n",
       "                        <td id=\"T_36729_row1_col3\" class=\"data row1 col3\" >6.30%</td>\n",
       "                        <td id=\"T_36729_row1_col4\" class=\"data row1 col4\" >3.98%</td>\n",
       "                        <td id=\"T_36729_row1_col5\" class=\"data row1 col5\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row1_col6\" class=\"data row1 col6\" >1.55%</td>\n",
       "                        <td id=\"T_36729_row1_col7\" class=\"data row1 col7\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row1_col8\" class=\"data row1 col8\" >1.20%</td>\n",
       "                        <td id=\"T_36729_row1_col9\" class=\"data row1 col9\" >6.87%</td>\n",
       "                        <td id=\"T_36729_row1_col10\" class=\"data row1 col10\" >2.82%</td>\n",
       "                        <td id=\"T_36729_row1_col11\" class=\"data row1 col11\" >6.34%</td>\n",
       "                        <td id=\"T_36729_row1_col12\" class=\"data row1 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_36729_level0_row2\" class=\"row_heading level0 row2\" >BAX</th>\n",
       "                        <td id=\"T_36729_row2_col0\" class=\"data row2 col0\" >11.50%</td>\n",
       "                        <td id=\"T_36729_row2_col1\" class=\"data row2 col1\" >9.44%</td>\n",
       "                        <td id=\"T_36729_row2_col2\" class=\"data row2 col2\" >10.37%</td>\n",
       "                        <td id=\"T_36729_row2_col3\" class=\"data row2 col3\" >8.96%</td>\n",
       "                        <td id=\"T_36729_row2_col4\" class=\"data row2 col4\" >10.62%</td>\n",
       "                        <td id=\"T_36729_row2_col5\" class=\"data row2 col5\" >12.35%</td>\n",
       "                        <td id=\"T_36729_row2_col6\" class=\"data row2 col6\" >4.46%</td>\n",
       "                        <td id=\"T_36729_row2_col7\" class=\"data row2 col7\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row2_col8\" class=\"data row2 col8\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row2_col9\" class=\"data row2 col9\" >4.71%</td>\n",
       "                        <td id=\"T_36729_row2_col10\" class=\"data row2 col10\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row2_col11\" class=\"data row2 col11\" >4.34%</td>\n",
       "                        <td id=\"T_36729_row2_col12\" class=\"data row2 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_36729_level0_row3\" class=\"row_heading level0 row3\" >BMY</th>\n",
       "                        <td id=\"T_36729_row3_col0\" class=\"data row3 col0\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row3_col1\" class=\"data row3 col1\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row3_col2\" class=\"data row3 col2\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row3_col3\" class=\"data row3 col3\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row3_col4\" class=\"data row3 col4\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row3_col5\" class=\"data row3 col5\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row3_col6\" class=\"data row3 col6\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row3_col7\" class=\"data row3 col7\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row3_col8\" class=\"data row3 col8\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row3_col9\" class=\"data row3 col9\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row3_col10\" class=\"data row3 col10\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row3_col11\" class=\"data row3 col11\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row3_col12\" class=\"data row3 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_36729_level0_row4\" class=\"row_heading level0 row4\" >CMCSA</th>\n",
       "                        <td id=\"T_36729_row4_col0\" class=\"data row4 col0\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row4_col1\" class=\"data row4 col1\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row4_col2\" class=\"data row4 col2\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row4_col3\" class=\"data row4 col3\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row4_col4\" class=\"data row4 col4\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row4_col5\" class=\"data row4 col5\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row4_col6\" class=\"data row4 col6\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row4_col7\" class=\"data row4 col7\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row4_col8\" class=\"data row4 col8\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row4_col9\" class=\"data row4 col9\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row4_col10\" class=\"data row4 col10\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row4_col11\" class=\"data row4 col11\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row4_col12\" class=\"data row4 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_36729_level0_row5\" class=\"row_heading level0 row5\" >CNP</th>\n",
       "                        <td id=\"T_36729_row5_col0\" class=\"data row5 col0\" >8.48%</td>\n",
       "                        <td id=\"T_36729_row5_col1\" class=\"data row5 col1\" >7.25%</td>\n",
       "                        <td id=\"T_36729_row5_col2\" class=\"data row5 col2\" >9.15%</td>\n",
       "                        <td id=\"T_36729_row5_col3\" class=\"data row5 col3\" >6.00%</td>\n",
       "                        <td id=\"T_36729_row5_col4\" class=\"data row5 col4\" >9.38%</td>\n",
       "                        <td id=\"T_36729_row5_col5\" class=\"data row5 col5\" >7.49%</td>\n",
       "                        <td id=\"T_36729_row5_col6\" class=\"data row5 col6\" >30.26%</td>\n",
       "                        <td id=\"T_36729_row5_col7\" class=\"data row5 col7\" >28.96%</td>\n",
       "                        <td id=\"T_36729_row5_col8\" class=\"data row5 col8\" >56.01%</td>\n",
       "                        <td id=\"T_36729_row5_col9\" class=\"data row5 col9\" >13.71%</td>\n",
       "                        <td id=\"T_36729_row5_col10\" class=\"data row5 col10\" >32.93%</td>\n",
       "                        <td id=\"T_36729_row5_col11\" class=\"data row5 col11\" >18.16%</td>\n",
       "                        <td id=\"T_36729_row5_col12\" class=\"data row5 col12\" >42.52%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_36729_level0_row6\" class=\"row_heading level0 row6\" >CPB</th>\n",
       "                        <td id=\"T_36729_row6_col0\" class=\"data row6 col0\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row6_col1\" class=\"data row6 col1\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row6_col2\" class=\"data row6 col2\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row6_col3\" class=\"data row6 col3\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row6_col4\" class=\"data row6 col4\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row6_col5\" class=\"data row6 col5\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row6_col6\" class=\"data row6 col6\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row6_col7\" class=\"data row6 col7\" >5.37%</td>\n",
       "                        <td id=\"T_36729_row6_col8\" class=\"data row6 col8\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row6_col9\" class=\"data row6 col9\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row6_col10\" class=\"data row6 col10\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row6_col11\" class=\"data row6 col11\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row6_col12\" class=\"data row6 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_36729_level0_row7\" class=\"row_heading level0 row7\" >DE</th>\n",
       "                        <td id=\"T_36729_row7_col0\" class=\"data row7 col0\" >3.82%</td>\n",
       "                        <td id=\"T_36729_row7_col1\" class=\"data row7 col1\" >2.72%</td>\n",
       "                        <td id=\"T_36729_row7_col2\" class=\"data row7 col2\" >1.18%</td>\n",
       "                        <td id=\"T_36729_row7_col3\" class=\"data row7 col3\" >2.32%</td>\n",
       "                        <td id=\"T_36729_row7_col4\" class=\"data row7 col4\" >0.90%</td>\n",
       "                        <td id=\"T_36729_row7_col5\" class=\"data row7 col5\" >5.14%</td>\n",
       "                        <td id=\"T_36729_row7_col6\" class=\"data row7 col6\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row7_col7\" class=\"data row7 col7\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row7_col8\" class=\"data row7 col8\" >13.08%</td>\n",
       "                        <td id=\"T_36729_row7_col9\" class=\"data row7 col9\" >1.55%</td>\n",
       "                        <td id=\"T_36729_row7_col10\" class=\"data row7 col10\" >0.82%</td>\n",
       "                        <td id=\"T_36729_row7_col11\" class=\"data row7 col11\" >1.01%</td>\n",
       "                        <td id=\"T_36729_row7_col12\" class=\"data row7 col12\" >3.21%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_36729_level0_row8\" class=\"row_heading level0 row8\" >HPQ</th>\n",
       "                        <td id=\"T_36729_row8_col0\" class=\"data row8 col0\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row8_col1\" class=\"data row8 col1\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row8_col2\" class=\"data row8 col2\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row8_col3\" class=\"data row8 col3\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row8_col4\" class=\"data row8 col4\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row8_col5\" class=\"data row8 col5\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row8_col6\" class=\"data row8 col6\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row8_col7\" class=\"data row8 col7\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row8_col8\" class=\"data row8 col8\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row8_col9\" class=\"data row8 col9\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row8_col10\" class=\"data row8 col10\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row8_col11\" class=\"data row8 col11\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row8_col12\" class=\"data row8 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_36729_level0_row9\" class=\"row_heading level0 row9\" >JCI</th>\n",
       "                        <td id=\"T_36729_row9_col0\" class=\"data row9 col0\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row9_col1\" class=\"data row9 col1\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row9_col2\" class=\"data row9 col2\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row9_col3\" class=\"data row9 col3\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row9_col4\" class=\"data row9 col4\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row9_col5\" class=\"data row9 col5\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row9_col6\" class=\"data row9 col6\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row9_col7\" class=\"data row9 col7\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row9_col8\" class=\"data row9 col8\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row9_col9\" class=\"data row9 col9\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row9_col10\" class=\"data row9 col10\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row9_col11\" class=\"data row9 col11\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row9_col12\" class=\"data row9 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_36729_level0_row10\" class=\"row_heading level0 row10\" >JPM</th>\n",
       "                        <td id=\"T_36729_row10_col0\" class=\"data row10 col0\" >8.63%</td>\n",
       "                        <td id=\"T_36729_row10_col1\" class=\"data row10 col1\" >14.80%</td>\n",
       "                        <td id=\"T_36729_row10_col2\" class=\"data row10 col2\" >8.82%</td>\n",
       "                        <td id=\"T_36729_row10_col3\" class=\"data row10 col3\" >16.70%</td>\n",
       "                        <td id=\"T_36729_row10_col4\" class=\"data row10 col4\" >8.46%</td>\n",
       "                        <td id=\"T_36729_row10_col5\" class=\"data row10 col5\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row10_col6\" class=\"data row10 col6\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row10_col7\" class=\"data row10 col7\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row10_col8\" class=\"data row10 col8\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row10_col9\" class=\"data row10 col9\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row10_col10\" class=\"data row10 col10\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row10_col11\" class=\"data row10 col11\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row10_col12\" class=\"data row10 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_36729_level0_row11\" class=\"row_heading level0 row11\" >LUV</th>\n",
       "                        <td id=\"T_36729_row11_col0\" class=\"data row11 col0\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row11_col1\" class=\"data row11 col1\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row11_col2\" class=\"data row11 col2\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row11_col3\" class=\"data row11 col3\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row11_col4\" class=\"data row11 col4\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row11_col5\" class=\"data row11 col5\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row11_col6\" class=\"data row11 col6\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row11_col7\" class=\"data row11 col7\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row11_col8\" class=\"data row11 col8\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row11_col9\" class=\"data row11 col9\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row11_col10\" class=\"data row11 col10\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row11_col11\" class=\"data row11 col11\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row11_col12\" class=\"data row11 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_36729_level0_row12\" class=\"row_heading level0 row12\" >MMC</th>\n",
       "                        <td id=\"T_36729_row12_col0\" class=\"data row12 col0\" >21.54%</td>\n",
       "                        <td id=\"T_36729_row12_col1\" class=\"data row12 col1\" >14.10%</td>\n",
       "                        <td id=\"T_36729_row12_col2\" class=\"data row12 col2\" >21.24%</td>\n",
       "                        <td id=\"T_36729_row12_col3\" class=\"data row12 col3\" >14.18%</td>\n",
       "                        <td id=\"T_36729_row12_col4\" class=\"data row12 col4\" >21.58%</td>\n",
       "                        <td id=\"T_36729_row12_col5\" class=\"data row12 col5\" >28.82%</td>\n",
       "                        <td id=\"T_36729_row12_col6\" class=\"data row12 col6\" >15.48%</td>\n",
       "                        <td id=\"T_36729_row12_col7\" class=\"data row12 col7\" >31.77%</td>\n",
       "                        <td id=\"T_36729_row12_col8\" class=\"data row12 col8\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row12_col9\" class=\"data row12 col9\" >17.66%</td>\n",
       "                        <td id=\"T_36729_row12_col10\" class=\"data row12 col10\" >7.46%</td>\n",
       "                        <td id=\"T_36729_row12_col11\" class=\"data row12 col11\" >15.14%</td>\n",
       "                        <td id=\"T_36729_row12_col12\" class=\"data row12 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_36729_level0_row13\" class=\"row_heading level0 row13\" >MO</th>\n",
       "                        <td id=\"T_36729_row13_col0\" class=\"data row13 col0\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row13_col1\" class=\"data row13 col1\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row13_col2\" class=\"data row13 col2\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row13_col3\" class=\"data row13 col3\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row13_col4\" class=\"data row13 col4\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row13_col5\" class=\"data row13 col5\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row13_col6\" class=\"data row13 col6\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row13_col7\" class=\"data row13 col7\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row13_col8\" class=\"data row13 col8\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row13_col9\" class=\"data row13 col9\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row13_col10\" class=\"data row13 col10\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row13_col11\" class=\"data row13 col11\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row13_col12\" class=\"data row13 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_36729_level0_row14\" class=\"row_heading level0 row14\" >MSFT</th>\n",
       "                        <td id=\"T_36729_row14_col0\" class=\"data row14 col0\" >17.59%</td>\n",
       "                        <td id=\"T_36729_row14_col1\" class=\"data row14 col1\" >21.37%</td>\n",
       "                        <td id=\"T_36729_row14_col2\" class=\"data row14 col2\" >22.47%</td>\n",
       "                        <td id=\"T_36729_row14_col3\" class=\"data row14 col3\" >20.90%</td>\n",
       "                        <td id=\"T_36729_row14_col4\" class=\"data row14 col4\" >22.75%</td>\n",
       "                        <td id=\"T_36729_row14_col5\" class=\"data row14 col5\" >18.64%</td>\n",
       "                        <td id=\"T_36729_row14_col6\" class=\"data row14 col6\" >34.22%</td>\n",
       "                        <td id=\"T_36729_row14_col7\" class=\"data row14 col7\" >8.89%</td>\n",
       "                        <td id=\"T_36729_row14_col8\" class=\"data row14 col8\" >29.71%</td>\n",
       "                        <td id=\"T_36729_row14_col9\" class=\"data row14 col9\" >40.09%</td>\n",
       "                        <td id=\"T_36729_row14_col10\" class=\"data row14 col10\" >44.96%</td>\n",
       "                        <td id=\"T_36729_row14_col11\" class=\"data row14 col11\" >42.31%</td>\n",
       "                        <td id=\"T_36729_row14_col12\" class=\"data row14 col12\" >52.28%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_36729_level0_row15\" class=\"row_heading level0 row15\" >NI</th>\n",
       "                        <td id=\"T_36729_row15_col0\" class=\"data row15 col0\" >10.83%</td>\n",
       "                        <td id=\"T_36729_row15_col1\" class=\"data row15 col1\" >12.00%</td>\n",
       "                        <td id=\"T_36729_row15_col2\" class=\"data row15 col2\" >10.47%</td>\n",
       "                        <td id=\"T_36729_row15_col3\" class=\"data row15 col3\" >12.53%</td>\n",
       "                        <td id=\"T_36729_row15_col4\" class=\"data row15 col4\" >10.43%</td>\n",
       "                        <td id=\"T_36729_row15_col5\" class=\"data row15 col5\" >12.69%</td>\n",
       "                        <td id=\"T_36729_row15_col6\" class=\"data row15 col6\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row15_col7\" class=\"data row15 col7\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row15_col8\" class=\"data row15 col8\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row15_col9\" class=\"data row15 col9\" >5.26%</td>\n",
       "                        <td id=\"T_36729_row15_col10\" class=\"data row15 col10\" >3.35%</td>\n",
       "                        <td id=\"T_36729_row15_col11\" class=\"data row15 col11\" >3.89%</td>\n",
       "                        <td id=\"T_36729_row15_col12\" class=\"data row15 col12\" >0.99%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_36729_level0_row16\" class=\"row_heading level0 row16\" >PCAR</th>\n",
       "                        <td id=\"T_36729_row16_col0\" class=\"data row16 col0\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row16_col1\" class=\"data row16 col1\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row16_col2\" class=\"data row16 col2\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row16_col3\" class=\"data row16 col3\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row16_col4\" class=\"data row16 col4\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row16_col5\" class=\"data row16 col5\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row16_col6\" class=\"data row16 col6\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row16_col7\" class=\"data row16 col7\" >9.39%</td>\n",
       "                        <td id=\"T_36729_row16_col8\" class=\"data row16 col8\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row16_col9\" class=\"data row16 col9\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row16_col10\" class=\"data row16 col10\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row16_col11\" class=\"data row16 col11\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row16_col12\" class=\"data row16 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_36729_level0_row17\" class=\"row_heading level0 row17\" >PSA</th>\n",
       "                        <td id=\"T_36729_row17_col0\" class=\"data row17 col0\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row17_col1\" class=\"data row17 col1\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row17_col2\" class=\"data row17 col2\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row17_col3\" class=\"data row17 col3\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row17_col4\" class=\"data row17 col4\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row17_col5\" class=\"data row17 col5\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row17_col6\" class=\"data row17 col6\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row17_col7\" class=\"data row17 col7\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row17_col8\" class=\"data row17 col8\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row17_col9\" class=\"data row17 col9\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row17_col10\" class=\"data row17 col10\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row17_col11\" class=\"data row17 col11\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row17_col12\" class=\"data row17 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_36729_level0_row18\" class=\"row_heading level0 row18\" >SEE</th>\n",
       "                        <td id=\"T_36729_row18_col0\" class=\"data row18 col0\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row18_col1\" class=\"data row18 col1\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row18_col2\" class=\"data row18 col2\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row18_col3\" class=\"data row18 col3\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row18_col4\" class=\"data row18 col4\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row18_col5\" class=\"data row18 col5\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row18_col6\" class=\"data row18 col6\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row18_col7\" class=\"data row18 col7\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row18_col8\" class=\"data row18 col8\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row18_col9\" class=\"data row18 col9\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row18_col10\" class=\"data row18 col10\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row18_col11\" class=\"data row18 col11\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row18_col12\" class=\"data row18 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_36729_level0_row19\" class=\"row_heading level0 row19\" >T</th>\n",
       "                        <td id=\"T_36729_row19_col0\" class=\"data row19 col0\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row19_col1\" class=\"data row19 col1\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row19_col2\" class=\"data row19 col2\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row19_col3\" class=\"data row19 col3\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row19_col4\" class=\"data row19 col4\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row19_col5\" class=\"data row19 col5\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row19_col6\" class=\"data row19 col6\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row19_col7\" class=\"data row19 col7\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row19_col8\" class=\"data row19 col8\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row19_col9\" class=\"data row19 col9\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row19_col10\" class=\"data row19 col10\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row19_col11\" class=\"data row19 col11\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row19_col12\" class=\"data row19 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_36729_level0_row20\" class=\"row_heading level0 row20\" >TGT</th>\n",
       "                        <td id=\"T_36729_row20_col0\" class=\"data row20 col0\" >7.18%</td>\n",
       "                        <td id=\"T_36729_row20_col1\" class=\"data row20 col1\" >4.75%</td>\n",
       "                        <td id=\"T_36729_row20_col2\" class=\"data row20 col2\" >8.91%</td>\n",
       "                        <td id=\"T_36729_row20_col3\" class=\"data row20 col3\" >5.86%</td>\n",
       "                        <td id=\"T_36729_row20_col4\" class=\"data row20 col4\" >9.26%</td>\n",
       "                        <td id=\"T_36729_row20_col5\" class=\"data row20 col5\" >11.20%</td>\n",
       "                        <td id=\"T_36729_row20_col6\" class=\"data row20 col6\" >14.02%</td>\n",
       "                        <td id=\"T_36729_row20_col7\" class=\"data row20 col7\" >15.63%</td>\n",
       "                        <td id=\"T_36729_row20_col8\" class=\"data row20 col8\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row20_col9\" class=\"data row20 col9\" >1.35%</td>\n",
       "                        <td id=\"T_36729_row20_col10\" class=\"data row20 col10\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row20_col11\" class=\"data row20 col11\" >0.49%</td>\n",
       "                        <td id=\"T_36729_row20_col12\" class=\"data row20 col12\" >0.29%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_36729_level0_row21\" class=\"row_heading level0 row21\" >TMO</th>\n",
       "                        <td id=\"T_36729_row21_col0\" class=\"data row21 col0\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row21_col1\" class=\"data row21 col1\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row21_col2\" class=\"data row21 col2\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row21_col3\" class=\"data row21 col3\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row21_col4\" class=\"data row21 col4\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row21_col5\" class=\"data row21 col5\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row21_col6\" class=\"data row21 col6\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row21_col7\" class=\"data row21 col7\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row21_col8\" class=\"data row21 col8\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row21_col9\" class=\"data row21 col9\" >0.58%</td>\n",
       "                        <td id=\"T_36729_row21_col10\" class=\"data row21 col10\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row21_col11\" class=\"data row21 col11\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row21_col12\" class=\"data row21 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_36729_level0_row22\" class=\"row_heading level0 row22\" >TXT</th>\n",
       "                        <td id=\"T_36729_row22_col0\" class=\"data row22 col0\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row22_col1\" class=\"data row22 col1\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row22_col2\" class=\"data row22 col2\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row22_col3\" class=\"data row22 col3\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row22_col4\" class=\"data row22 col4\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row22_col5\" class=\"data row22 col5\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row22_col6\" class=\"data row22 col6\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row22_col7\" class=\"data row22 col7\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row22_col8\" class=\"data row22 col8\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row22_col9\" class=\"data row22 col9\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row22_col10\" class=\"data row22 col10\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row22_col11\" class=\"data row22 col11\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row22_col12\" class=\"data row22 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_36729_level0_row23\" class=\"row_heading level0 row23\" >VZ</th>\n",
       "                        <td id=\"T_36729_row23_col0\" class=\"data row23 col0\" >4.27%</td>\n",
       "                        <td id=\"T_36729_row23_col1\" class=\"data row23 col1\" >5.99%</td>\n",
       "                        <td id=\"T_36729_row23_col2\" class=\"data row23 col2\" >3.00%</td>\n",
       "                        <td id=\"T_36729_row23_col3\" class=\"data row23 col3\" >6.25%</td>\n",
       "                        <td id=\"T_36729_row23_col4\" class=\"data row23 col4\" >2.63%</td>\n",
       "                        <td id=\"T_36729_row23_col5\" class=\"data row23 col5\" >3.68%</td>\n",
       "                        <td id=\"T_36729_row23_col6\" class=\"data row23 col6\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row23_col7\" class=\"data row23 col7\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row23_col8\" class=\"data row23 col8\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row23_col9\" class=\"data row23 col9\" >8.18%</td>\n",
       "                        <td id=\"T_36729_row23_col10\" class=\"data row23 col10\" >7.65%</td>\n",
       "                        <td id=\"T_36729_row23_col11\" class=\"data row23 col11\" >8.32%</td>\n",
       "                        <td id=\"T_36729_row23_col12\" class=\"data row23 col12\" >0.72%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_36729_level0_row24\" class=\"row_heading level0 row24\" >ZION</th>\n",
       "                        <td id=\"T_36729_row24_col0\" class=\"data row24 col0\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row24_col1\" class=\"data row24 col1\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row24_col2\" class=\"data row24 col2\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row24_col3\" class=\"data row24 col3\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row24_col4\" class=\"data row24 col4\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row24_col5\" class=\"data row24 col5\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row24_col6\" class=\"data row24 col6\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row24_col7\" class=\"data row24 col7\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row24_col8\" class=\"data row24 col8\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row24_col9\" class=\"data row24 col9\" >0.03%</td>\n",
       "                        <td id=\"T_36729_row24_col10\" class=\"data row24 col10\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row24_col11\" class=\"data row24 col11\" >0.00%</td>\n",
       "                        <td id=\"T_36729_row24_col12\" class=\"data row24 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fdcd7412c10>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_s.style.format(\"{:.2%}\").background_gradient(cmap='YlGn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plotting a comparison of assets weights for each portfolio\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig.set_figwidth(14)\n",
    "fig.set_figheight(6)\n",
    "ax = fig.subplots(nrows=1, ncols=1)\n",
    "\n",
    "w_s.plot.bar(ax=ax)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
